2023

Jump to: 2022, 2021, 2020

23 pages, 4416 KiB

Open AccessArticle

Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting

by Mojtaba Yari, Danial Jahed Armaghani, Chrysanthos Maraveas, Alireza Nouri Ejlali, Edy Tonnizam Mohamad and Panagiotis G. Asteris

Appl. Sci. 2023, 13(3), 1345; https://doi.org/10.3390/app13031345 - 19 Jan 2023

Cited by 12 | Viewed by 1470

Abstract

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting [...] Read more.

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone’s safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R²) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock. Full article

► Show Figures

Figure 1

2022

Jump to: 2023, 2021, 2020

35 pages, 3872 KiB

Open AccessArticle

Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art

by Chrysanthos Maraveas

Appl. Sci. 2023, 13(1), 14; https://doi.org/10.3390/app13010014 - 20 Dec 2022

Cited by 21 | Viewed by 9476

Abstract

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems [...] Read more.

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability. Full article

► Show Figures

Figure 1

15 pages, 3329 KiB

Open AccessArticle

Predicting the Young’s Modulus of Rock Material Based on Petrographic and Rock Index Tests Using Boosting and Bagging Intelligence Techniques

by Long Tsang, Biao He, Ahmad Safuan A Rashid, Abduladheem Turki Jalil and Mohanad Muayad Sabri Sabri

Appl. Sci. 2022, 12(20), 10258; https://doi.org/10.3390/app122010258 - 12 Oct 2022

Cited by 5 | Viewed by 1486

Abstract

Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for [...] Read more.

Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young’s modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study; secondly, boosting models outperformed the bagging models. Full article

► Show Figures

Figure 1

23 pages, 8160 KiB

Open AccessArticle

Developing Predictive Models of Collapse Settlement and Coefficient of Stress Release of Sandy-Gravel Soil via Evolutionary Polynomial Regression

by Ali Reza Ghanizadeh, Ali Delaram, Pouyan Fakharian and Danial Jahed Armaghani

Appl. Sci. 2022, 12(19), 9986; https://doi.org/10.3390/app12199986 - 04 Oct 2022

Cited by 28 | Viewed by 2065

Abstract

The collapse settlement of granular soil, which brings about considerable deformations, is an important issue in geotechnical engineering. Several factors are involved in this phenomenon, which makes it difficult to predict. The present study aimed to develop a model to predict the collapse [...] Read more.

The collapse settlement of granular soil, which brings about considerable deformations, is an important issue in geotechnical engineering. Several factors are involved in this phenomenon, which makes it difficult to predict. The present study aimed to develop a model to predict the collapse settlement and coefficient of stress release of sandy gravel soil through evolutionary polynomial regression (EPR). To achieve this, a dataset containing 180 records obtained from a large-scale direct shear test was used. In this study, five models were developed with the secant hyperbolic, tangent hyperbolic, natural logarithm, exponential, and sinusoidal inner functions. Using sand content (SC), normal stress (

σ_{n}

), shear stress level (SL), and relative density (Dr) values, the models can predict the collapse settlement (∆H) and coefficient of stress release (CSR). The results indicated that the models developed with the exponential functions were the best models. With these models, the values of

R^{2}

for training, testing, and all data in the prediction of collapse settlement were 0.9759, 0.9759, and 0.9757, respectively, and the values of

R^{2}

in predicting the coefficient of stress release were 0.9833, 0.9820, and 0.9833, respectively. The sensitivity analysis also revealed that the sand content (SC) and relative density (Dr) parameters had the highest and lowest degrees of importance in predicting collapse settlement. In contrast, the Dr and SC parameters showed the highest and lowest degrees of importance in predicting the coefficient of stress release. Finally, the conducted parametric study showed that the developed models were in line with the results of previous studies. Full article

► Show Figures

Figure 1

21 pages, 734 KiB

Open AccessReview

An Overview of Variants and Advancements of PSO Algorithm

by Meetu Jain, Vibha Saihjpal, Narinder Singh and Satya Bir Singh

Appl. Sci. 2022, 12(17), 8392; https://doi.org/10.3390/app12178392 - 23 Aug 2022

Cited by 110 | Viewed by 7930

Abstract

Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained [...] Read more.

Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included. Full article

► Show Figures

Figure 1

28 pages, 19913 KiB

Open AccessArticle

Real Coded Mixed Integer Genetic Algorithm for Geometry Optimization of Flight Simulator Mechanism Based on Rotary Stewart Platform

by Miloš D. Petrašinović, Aleksandar M. Grbović, Danilo M. Petrašinović, Mihailo G. Petrović and Nikola G. Raičević

Appl. Sci. 2022, 12(14), 7085; https://doi.org/10.3390/app12147085 - 13 Jul 2022

Cited by 3 | Viewed by 1773

Abstract

Designing the motion platform for the flight simulator is closely coupled with the particular aircraft’s flight envelope. While in training, the pilot on the motion platform has to experience the same feeling as in the aircraft. That means that flight simulators need to [...] Read more.

Designing the motion platform for the flight simulator is closely coupled with the particular aircraft’s flight envelope. While in training, the pilot on the motion platform has to experience the same feeling as in the aircraft. That means that flight simulators need to simulate all flight cases and forces acting upon the pilot during flight. Among many existing mechanisms, parallel mechanisms based on the Stewart platform are suitable because they have six degrees of freedom. In this paper, a real coded mixed integer genetic algorithm (RCMIGA) is applied for geometry optimization of the Stewart platform with rotary actuators (6-RUS) to design a mechanism with appropriate physical limitations of workspace and motion performances. The chosen algorithm proved that it can find the best global solution with all imposed constraints. At the same time, the obtained geometry can be manufactured because integer solutions can be mapped to available discrete values. Geometry is defined with a minimum number of parameters that fully define the mechanism with all constraints. These geometric parameters are then optimized to obtain custom-tailored geometry for aircraft flight simulation. Full article

► Show Figures

Figure 1

22 pages, 1762 KiB

Open AccessReview

Potential Contribution of the Grey Wolf Optimization Algorithm in Reducing Active Power Losses in Electrical Power Systems

by Mohamed Abbas, Mohammed A. Alshehri and Abdulwasa Bakr Barnawi

Appl. Sci. 2022, 12(12), 6177; https://doi.org/10.3390/app12126177 - 17 Jun 2022

Cited by 8 | Viewed by 2298

Abstract

Active power losses have the potential to affect the distribution of power flows along transmission lines as well as the mix of energy used throughout power networks. Grey wolf optimization algorithms (GWOs) are used in electrical power systems to reduce active power losses. [...] Read more.

Active power losses have the potential to affect the distribution of power flows along transmission lines as well as the mix of energy used throughout power networks. Grey wolf optimization algorithms (GWOs) are used in electrical power systems to reduce active power losses. GWOs are straightforward algorithms to implement because of their simple structure, low storage and computing needs, and quicker convergence from the constant decrease in search space. The electrical power system may be separated into three primary components: generation, transmission, and distribution. Each component of the power system is critical in the process of distributing electricity from where it is produced to where it is used by customers. By using the GWO, it is possible to regulate the active power delivered by a high-voltage direct current network based on a multi-terminal voltage-source converter. This review focuses on the role of GWO in reducing the amount of active power lost in power systems by considering the three major components of electrical power systems. Additionally, this work discusses the significance of GWO in minimizing active power losses in all components of the electrical power system. Results show that GWO plays a key role in reducing active power losses and consequently reducing the impact of power losses on the performance of electrical components by different percentages. Depending on how the power system is set up, the amount of reduction can be anywhere from 12% to 65.5%. Full article

► Show Figures

Figure 1

17 pages, 2841 KiB

Open AccessArticle

Order Releasing and Scheduling for a Multi-Item MTO Industry: An Efficient Heuristic Based on Drum Buffer Rope

by Lei Yue, Guangyan Xu, Jabir Mumtaz, Yarong Chen and Tao Zou

Appl. Sci. 2022, 12(4), 1925; https://doi.org/10.3390/app12041925 - 12 Feb 2022

Cited by 2 | Viewed by 1705

Abstract

Increasing productivity and efficiency in factories with make-to-order (MTO) production systems have attracted growing interest of academia and industry. In MTO companies, efficient order release and schedule are essential for succeeding in today’s marketplace. However, dynamic demand of customers and constrained resources make [...] Read more.

Increasing productivity and efficiency in factories with make-to-order (MTO) production systems have attracted growing interest of academia and industry. In MTO companies, efficient order release and schedule are essential for succeeding in today’s marketplace. However, dynamic demand of customers and constrained resources make it difficult to achieve, as well as limiting the profits. Thus, to overcome the problem of order releasing and multi-item scheduling considering the capacity constrained resources investigated, a heuristic approach is proposed based on the drum-buffer-rope (DBR) method. The proposed heuristic is tested on different types of problems based on due date tightness and demand of products. The performance of the proposed heuristic is compared with other famous heuristic methods in literature. End results indicate that the proposed heuristic based on the DBR method outperforms against the other competitors, and it gives more significant results when optimal buffer size is adopted. Full article

► Show Figures

Figure 1

2021

Jump to: 2023, 2022, 2020

24 pages, 9146 KiB

Open AccessArticle

Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints

by Jun-Hee Han and Ju-Yong Lee

Appl. Sci. 2021, 11(23), 11240; https://doi.org/10.3390/app112311240 - 26 Nov 2021

Cited by 4 | Viewed by 1575

Abstract

This study investigates a two-stage assembly-type flow shop with limited waiting time constraints for minimizing the makespan. The first stage consists of m machines fabricating m types of components, whereas the second stage has a single machine to assemble the components into the [...] Read more.

This study investigates a two-stage assembly-type flow shop with limited waiting time constraints for minimizing the makespan. The first stage consists of m machines fabricating m types of components, whereas the second stage has a single machine to assemble the components into the final product. In the flow shop, the assembly operations in the second stage should start within the limited waiting times after those components complete in the first stage. For this problem, a mixed-integer programming formulation is provided, and this formulation is used to find an optimal solution using a commercial optimization solver CPLEX. As this problem is proved to be NP-hard, various heuristic algorithms (priority rule-based list scheduling, constructive heuristic, and metaheuristic) are proposed to solve a large-scale problem within a short computation time. To evaluate the proposed algorithms, a series of computational experiments, including the calibration of the metaheuristics, were performed on randomly generated problem instances, and the results showed outperformance of the proposed iterated greedy algorithm and simulated annealing algorithm in small- and large-sized problems, respectively. Full article

► Show Figures

Figure 1

21 pages, 6054 KiB

Open AccessArticle

Gate Attentional Factorization Machines: An Efficient Neural Network Considering Both Accuracy and Speed

by Huaidong Yu, Jian Yin and Yan Li

Appl. Sci. 2021, 11(20), 9546; https://doi.org/10.3390/app11209546 - 14 Oct 2021

Cited by 3 | Viewed by 1400

Abstract

Nowadays, to deal with the increasing data of users and items and better mine the potential relationship between the data, the model used by the recommendation system has become more and more complex. In this case, how to ensure the prediction accuracy and [...] Read more.

Nowadays, to deal with the increasing data of users and items and better mine the potential relationship between the data, the model used by the recommendation system has become more and more complex. In this case, how to ensure the prediction accuracy and operation speed of the recommendation system has become an urgent problem. Deep neural network is a good solution to the problem of accuracy, we can use more network layers, more advanced feature cross way to improve the utilization of data. However, when the accuracy is guaranteed, little attention is paid to the speed problem. We can only pursue better machine efficiency, and we do not pay enough attention to the speed efficiency of the model itself. Some models with advantages in speed, such as PNN, are slightly inferior in accuracy. In this paper, the Gate Attention Factorization Machine (GAFM) model based on the double factors of accuracy and speed is proposed, and the structure of gate is used to control the speed and accuracy. Extensive experiments have been conducted on data sets in various application scenarios, and the results show that the GAFM model is better than the existing factorization machines in both speed and accuracy. Full article

► Show Figures

Figure 1

14 pages, 933 KiB

Open AccessArticle

COMPACT: Concurrent or Ordered Matrix-Based Packing Arrangement Computation Technique

by Gokhan Serhat

Appl. Sci. 2021, 11(11), 5217; https://doi.org/10.3390/app11115217 - 04 Jun 2021

Viewed by 2076

Abstract

Despite their versatility in treating irregular geometries, the raster methods have received limited attention in solving packing problems involving rotatable objects. In addition, raster approximation allows the use of unique performance metrics and indirect consideration of constraints, which have not been exploited in [...] Read more.

Despite their versatility in treating irregular geometries, the raster methods have received limited attention in solving packing problems involving rotatable objects. In addition, raster approximation allows the use of unique performance metrics and indirect consideration of constraints, which have not been exploited in the literature. This study presents the Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique (COMPACT). The method allows the objects to be rotated by arbitrary angles, unlike the right-angled rotation restrictions imposed in many existing packing optimization studies based on raster methods. The raster approximations are obtained through loop-free operations that improve efficiency. Additionally, a novel performance metric is introduced, which favors efficient filling of the available space by maximizing the overall contact within the domain. Moreover, the objective functions are exploited to discard the overlap and overflow constraints and enable the use of unconstrained optimization methods. The results of the case studies demonstrate the effectiveness of the proposed technique. Full article

► Show Figures

Graphical abstract

19 pages, 3490 KiB

Open AccessArticle

Priority Pricing for Efficient Resource Usage of Mobile Internet Access

by Sung-woo Cho

Appl. Sci. 2021, 11(9), 4083; https://doi.org/10.3390/app11094083 - 29 Apr 2021

Viewed by 1398

Abstract

Radio-frequency spectrum resources are finite and scarce, but their demand is increasing exponentially every year. Therefore, wireless network resources are too expensive to be wasted. To avoid waste, pricing techniques can efficiently control resource usage and manage user needs in networks. This study [...] Read more.

Radio-frequency spectrum resources are finite and scarce, but their demand is increasing exponentially every year. Therefore, wireless network resources are too expensive to be wasted. To avoid waste, pricing techniques can efficiently control resource usage and manage user needs in networks. This study focuses on QoS-aware pricing for usage-based mobile Internet access charging. Specifically, I propose a heuristic algorithm for priority pricing with multiple service levels. The proposed algorithm is built on top of the existing equilibrium analysis methods. While being extensively studied for optimal price selection, the equilibrium methods make a few unrealistic assumptions, and so my methods adjust the solutions of the equilibrium methods to account for distortions that the real world creates. The evaluation results indicate that multiple equilibrium prices may exist, and the proposed scheme produces a pricing plan that is substantially more effective than existing equilibrium methods. Full article

► Show Figures

Figure 1

17 pages, 3592 KiB

Open AccessArticle

Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

by Jie Zeng, Panayiotis C. Roussis, Ahmed Salih Mohammed, Chrysanthos Maraveas, Seyed Alireza Fatemi, Danial Jahed Armaghani and Panagiotis G. Asteris

Appl. Sci. 2021, 11(8), 3705; https://doi.org/10.3390/app11083705 - 20 Apr 2021

Cited by 31 | Viewed by 2808

Abstract

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was [...] Read more.

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values. Full article

► Show Figures

Figure 1

41 pages, 1378 KiB

Open AccessArticle

An Algorithm for Rescheduling of Trains under Planned Track Closures

by Grzegorz Filcek, Dariusz Gąsior, Maciej Hojda and Jerzy Józefczyk

Appl. Sci. 2021, 11(5), 2334; https://doi.org/10.3390/app11052334 - 06 Mar 2021

Cited by 3 | Viewed by 2366

Abstract

This work considered a joint problem of train rescheduling and closure planning. The derivation of a new train run schedule and the determination of a closure plan not only must guarantee the satisfaction of all the given constraints but also must optimize the [...] Read more.

This work considered a joint problem of train rescheduling and closure planning. The derivation of a new train run schedule and the determination of a closure plan not only must guarantee the satisfaction of all the given constraints but also must optimize the number of accepted closures, the number of approved train runs, and the total time shift between the resultant and the original schedule. Presented is a novel nonlinear mixed integer optimization problem which is valid for a broad class of railway networks. A multi-level hierarchical heuristic algorithm is introduced due to the NP-hardness of the considered optimization problem. The algorithm is able, on an iterative basis, to jointly select closures and train runs, along with the derivation of a train schedule. Results obtained by the algorithm, launched for the conducted experiments, confirm its ability to provide acceptable and feasible solutions in a reasonable amount of time. Full article

► Show Figures

Figure 1

20 pages, 522 KiB

Open AccessArticle

Adaptive Multi-Level Search for Global Optimization: An Integrated Swarm Intelligence-Metamodelling Technique

by Guirong Dong, Chengyang Liu, Dianzi Liu and Xiaoan Mao

Appl. Sci. 2021, 11(5), 2277; https://doi.org/10.3390/app11052277 - 04 Mar 2021

Cited by 5 | Viewed by 1847

Abstract

Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method [...] Read more.

Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method (FEM) or computational fluid dynamics (CFD), by which function evaluations of population-based algorithms are repetitively computed to seek a global optimum. It is noted that these simulations become computationally prohibitive for design optimization of complex structures. To efficiently and effectively address this class of problems, an adaptively integrated swarm intelligence-metamodelling (ASIM) technique enabling multi-level search and model management for the optimal solution is proposed in this paper. The developed technique comprises two steps: in the first step, a global-level exploration for near optimal solution is performed by adaptive swarm-intelligence algorithm, and in the second step, a local-level exploitation for the fine optimal solution is studied on adaptive metamodels, which are constructed by the multipoint approximation method (MAM). To demonstrate the superiority of the proposed technique over other methods, such as conventional MAM, particle swarm optimization, hybrid cuckoo search, and water cycle algorithm in terms of computational expense associated with solving complex optimization problems, one benchmark mathematical example and two real-world complex design problems are examined. In particular, the key factors responsible for the balance between exploration and exploitation are discussed as well. Full article

► Show Figures

Figure 1

19 pages, 2956 KiB

Open AccessArticle

A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization

by Jian Dong, Zhiyu Wang and Jinjun Mo

Appl. Sci. 2021, 11(5), 2243; https://doi.org/10.3390/app11052243 - 03 Mar 2021

Cited by 9 | Viewed by 1950

Abstract

This paper proposes a phase angle-modulated bat algorithm (P-AMBA) for high-dimensional binary optimization. The idea was to reduce the optimization time by introducing angle modulation technology to reduce the optimization dimensions. Different from the original angle-modulated bat algorithm (AMBA), the control of the [...] Read more.

This paper proposes a phase angle-modulated bat algorithm (P-AMBA) for high-dimensional binary optimization. The idea was to reduce the optimization time by introducing angle modulation technology to reduce the optimization dimensions. Different from the original angle-modulated bat algorithm (AMBA), the control of the trigonometric generating function cosine wave is by introducing new parameters, thereby improving the perturbation ability of the function curve near the x-axis. P-AMBA can explore more 0/1 solutions, and it has advantages in optimizing convergence speed and global search capabilities. The numerical results of the 0–1 knapsack problem tests show that P-AMBA is superior to the contrast algorithms on optimization ability and optimization time. Finally, the experimental result of a compact dual-band planar monopole antenna design showed the effectiveness of P-AMBA in engineering applications. Full article

► Show Figures

Figure 1

19 pages, 2015 KiB

Open AccessArticle

Finding Effective Item Assignment Plans with Weighted Item Associations Using A Hybrid Genetic Algorithm

by Minho Ryu, Kwang-Il Ahn and Kichun Lee

Appl. Sci. 2021, 11(5), 2209; https://doi.org/10.3390/app11052209 - 03 Mar 2021

Cited by 2 | Viewed by 1796

Abstract

By identifying useful relationships between massive datasets, association rule mining can provide new insights to decision-makers. Item assignment models based on association between items are used to place items in a retail or e-commerce environment to increase sales. However, existing models fail to [...] Read more.

By identifying useful relationships between massive datasets, association rule mining can provide new insights to decision-makers. Item assignment models based on association between items are used to place items in a retail or e-commerce environment to increase sales. However, existing models fail to combine these associations with item-specific information, such as profit and purchasing frequency. To find effective assignments with item-specific information, we propose a new hybrid genetic algorithm that incorporates a robust tabu search with a novel rectangular partially matched crossover, focusing on rectangular layouts. Interestingly, we show that our item assignment model is equivalent to popular quadratic assignment NP-hard problems. We show the effectiveness of the proposed algorithm, using benchmark instances from QAPLIB and synthetic databases that represent real-life retail situations, and compare our algorithm with other existing algorithms. We also show that the proposed crossover operator outperforms a few existing ones in both fitness values and search times. The experimental results show that not only does the proposed item assignment model generates a more profitable assignment plan than the other tested models based on association alone but it also obtains better solutions than the other tested algorithms. Full article

► Show Figures

Figure 1

23 pages, 5500 KiB

Open AccessArticle

An Attraction Map Framework of a Complex Multi-Echelon Vehicle Routing Problem with Random Walk Analysis

by Anita Agárdi, László Kovács and Tamás Bányai

Appl. Sci. 2021, 11(5), 2100; https://doi.org/10.3390/app11052100 - 27 Feb 2021

Cited by 9 | Viewed by 1993

Abstract

The paper aims to investigate the basin of attraction map of a complex Vehicle Routing Problem with random walk analysis. The Vehicle Routing Problem (VRP) is a common discrete optimization problem in field of logistics. In the case of the base VRP, the [...] Read more.

The paper aims to investigate the basin of attraction map of a complex Vehicle Routing Problem with random walk analysis. The Vehicle Routing Problem (VRP) is a common discrete optimization problem in field of logistics. In the case of the base VRP, the positions of one single depot and many customers (which have product demands) are given. The vehicles and their capacity limits are also fixed in the system and the objective function is the minimization of the length of the route. In the literature, many approaches have appeared to simulate the transportation demands. Most of the approaches are using some kind of metaheuristics. Solving the problems with metaheuristics requires exploring the fitness landscape of the optimization problem. The fitness landscape analysis consists of the investigation of the following elements: the set of the possible states, the fitness function and the neighborhood relationship. We use also metaheuristics are used to perform neighborhood discovery depending on the neighborhood interpretation. In this article, the following neighborhood operators are used for the basin of attraction map: 2-opt, Order Crossover (OX), Partially Matched Crossover (PMX), Cycle Crossover (CX). Based on our test results, the 2-opt and Partially Matched Crossover operators are more efficient than the Order Crossover and Cycle Crossovers. Full article

► Show Figures

Figure 1

21 pages, 14450 KiB

Open AccessArticle

Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope

by Wei Wei, Xibing Li, Jingzhi Liu, Yaodong Zhou, Lu Li and Jian Zhou

Appl. Sci. 2021, 11(4), 1922; https://doi.org/10.3390/app11041922 - 22 Feb 2021

Cited by 16 | Viewed by 2415

Abstract

To detect areas with the potential for landslides, slopes are routinely subjected to stability analyses. To this end, there is a need to adopt appropriate mitigation techniques. In general, the stability of slopes with circular failure mode is defined as the factor of [...] Read more.

To detect areas with the potential for landslides, slopes are routinely subjected to stability analyses. To this end, there is a need to adopt appropriate mitigation techniques. In general, the stability of slopes with circular failure mode is defined as the factor of safety (FOS). The literature includes a variety of numerical/analytical models proposed in different studies to compute the FOS values of slopes. However, the main challenge is to propose a model for solving a non-linear relationship between independent parameters (which have a great impact on slope stability) and FOS values of slopes. This creates a problem with a high level of complexity and with multiple variables. To resolve the problem, this study proposes a new hybrid intelligent model for FOS evaluation and analysis of slopes in two different phases: simulation and optimization. In the simulation phase, different support vector regression (SVR) kernels were built to predict FOS values. The results showed that the radius basis function (RBF) kernel produces more accurate performance prediction compared with the other applied kernels. The prediction accuracy of this kernel was obtained as coefficient of determination = 0.94, which indicates a high prediction capacity during the simulation phase. Then, in the optimization phase, the proposed SVR model was optimized through the use of two well-known techniques, namely, the whale optimization algorithm (WOA) and Harris hawks optimization (HHO), and the optimum input parameters were obtained. The optimal results confirmed that both optimization techniques are able to achieve a high value for FOS of slopes; however, the HHO shows a more powerful process in FOS maximization compared with the WOA technique. In addition, the developed model was also successfully validated using new data with nine data samples. Full article

► Show Figures

Figure 1

16 pages, 5684 KiB

Open AccessArticle

The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

by Jie Zeng, Panagiotis G. Asteris, Anna P. Mamou, Ahmed Salih Mohammed, Emmanuil A. Golias, Danial Jahed Armaghani, Koohyar Faizi and Mahdi Hasanipanah

Appl. Sci. 2021, 11(3), 908; https://doi.org/10.3390/app11030908 - 20 Jan 2021

Cited by 30 | Viewed by 2523

Abstract

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on [...] Read more.

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models. Full article

► Show Figures

Figure 1

30 pages, 33936 KiB

Open AccessArticle

Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

by Mohammed Amin Benbouras and Alexandru-Ionut Petrisor

Appl. Sci. 2021, 11(2), 536; https://doi.org/10.3390/app11020536 - 07 Jan 2021

Cited by 12 | Viewed by 3533

Abstract

Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In [...] Read more.

Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies. Full article

► Show Figures

Graphical abstract

18 pages, 4155 KiB

Open AccessArticle

A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

by Hao Gu, Tengfei Wang, Yantao Zhu, Cheng Wang, Dashan Yang and Lixian Huang

Appl. Sci. 2021, 11(1), 463; https://doi.org/10.3390/app11010463 - 05 Jan 2021

Cited by 12 | Viewed by 3236

Abstract

A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly [...] Read more.

A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly among all the effect quantities. However, due to the change of the external environment, the failure of monitoring instruments, and the existence of human errors, the obtained deformation monitoring data usually miss pieces, and sometimes the missing pieces are so critical that the remaining data fail to fully reflect the actual deformation patterns. In this paper, the composition, characteristics, and contamination of the concrete dam deformation monitoring information are analyzed. From the single-value missing data completion method based on the nonlocal average method, a multi-value missing data completion method using BP (back propagation) mapping of spatial adjacent points is proposed to improve the accuracy of analysis and pattern prediction of concrete dam deformation behaviors. A case study is performed to validate the proposed method. Full article

► Show Figures

Figure 1

2020

Jump to: 2023, 2022, 2021

19 pages, 5844 KiB

Open AccessArticle

Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

by Xuan Ye, Lan Luo, Li Hou, Yang Duan and Yang Wu

Appl. Sci. 2020, 10(23), 8641; https://doi.org/10.3390/app10238641 - 03 Dec 2020

Cited by 13 | Viewed by 2213

Abstract

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection [...] Read more.

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach. Full article

► Show Figures

Figure 1

18 pages, 3450 KiB

Open AccessArticle

Multi-Objective, Reliability-Based Design Optimization of a Steering Linkage

by Suwin Sleesongsom and Sujin Bureerat

Appl. Sci. 2020, 10(17), 5748; https://doi.org/10.3390/app10175748 - 20 Aug 2020

Cited by 11 | Viewed by 2471

Abstract

Reliability-based design optimization (RBDO) of a mechanism is normally based on the non-probabilistic model, which is viewed as failure possibility constraints in each optimization loop. It leads to a double-loop nested problem that causes a computationally expensive evaluation. Several methods have been developed [...] Read more.

Reliability-based design optimization (RBDO) of a mechanism is normally based on the non-probabilistic model, which is viewed as failure possibility constraints in each optimization loop. It leads to a double-loop nested problem that causes a computationally expensive evaluation. Several methods have been developed to solve the problem, which are expected to increase the realization of optimum results and computational efficiency. The purpose of this paper was to develop a new technique of RBDO that can reduce the complexity of the double-loop nested problem to a single-loop. This involves using a multi-objective evolutionary technique combined with the worst-case scenario and fuzzy sets, known as a multi-objective, reliability-based design optimization (MORBDO). The optimization test problem and a steering linkage design were used to validate the performance of the proposed technique. The proposed technique can reduce the complexity of the design problem, producing results that are more conservative and realizable. Full article

► Show Figures

Figure 1

21 pages, 6176 KiB

Open AccessArticle

Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study

by Chee Soon Lim, Edy Tonnizam Mohamad, Mohammad Reza Motahari, Danial Jahed Armaghani and Rosli Saad

Appl. Sci. 2020, 10(17), 5734; https://doi.org/10.3390/app10175734 - 19 Aug 2020

Cited by 9 | Viewed by 2913

Abstract

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs [...] Read more.

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification. Full article

► Show Figures

Figure 1

23 pages, 4957 KiB

Open AccessArticle

Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt

by Thanh-Hai Le, Hoang-Long Nguyen, Binh Thai Pham, May Huu Nguyen, Cao-Thang Pham, Ngoc-Lan Nguyen, Tien-Thinh Le and Hai-Bang Ly

Appl. Sci. 2020, 10(15), 5242; https://doi.org/10.3390/app10155242 - 29 Jul 2020

Cited by 12 | Viewed by 3583

Abstract

Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature [...] Read more.

Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature by 30–50 °C, compared to conventional hot mix asphalt technology (HMA). The dynamic modulus |E*| has been acknowledged as a vital material property in the mechanistic-empirical design and analysis and further reflects the strains and displacements of such layered pavement structures. The objective of this study is twofold, aiming at favoring the potential use of SMA with WMA technique. To this aim, first, laboratory tests were conducted to compare the performance of SMA and HMA through the dynamic modulus. Second, an advanced hybrid artificial intelligence technique to accurately predict the dynamic modulus of asphalt mixtures was developed. This hybrid model (ANN-TLBO) was based on an Artificial Neural Network (ANN) algorithm and Teaching Learning Based Optimization (TLBO) technique. A database containing the as-obtained experimental tests (96 data) was used for the development and assessment of the ANN-TLBO model. The experimental results showed that SMA mixtures exhibited higher values of the dynamic modulus |E*| than HMA, and the WMA technology increased the dynamic modulus values compared with the hot technology. Furthermore, the proposed hybrid algorithm could successfully predict the dynamic modulus with remarkable values of R² of 0.989 and 0.985 for the training and testing datasets, respectively. Lastly, the effects of temperature and frequency on the dynamic modulus were evaluated and discussed. Full article

► Show Figures

Figure 1

19 pages, 3397 KiB

Open AccessArticle

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

by Saad Sh. Sammen, Mohammad Ali Ghorbani, Anurag Malik, Yazid Tikhamarine, Mohammad AmirRahmani, Nadhir Al-Ansari and Kwok-Wing Chau

Appl. Sci. 2020, 10(15), 5160; https://doi.org/10.3390/app10155160 - 27 Jul 2020

Cited by 56 | Viewed by 3538

Abstract

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the [...] Read more.

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Heuristic Algorithms in Engineering and Applied Sciences

Share This Topical Collection

Editor

Topical Collection Information

Keywords

Published Papers (27 papers)

2023

Jump to: 2022, 2021, 2020

2022

Jump to: 2023, 2021, 2020

2021

Jump to: 2023, 2022, 2020

2020

Jump to: 2023, 2022, 2021

Further Information

Guidelines

MDPI Initiatives

Follow MDPI