Evolutionary Process for Engineering Optimization (II)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 26065

Special Issue Editors


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Guest Editor
1. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
2. University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
Interests: data analytics; machine learning; evolutionary computation; engineering optimization
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Guest Editor
Faculty of Information Technology, Al Al-Bayt University, Mafraq, Jordan
Interests: arithmetic optimization algorithm (AOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling; optimization algorithms; evolutionary computations; information retrieval; text clustering; feature selection; combinatorial problems; optimization; advanced machine learning; big data; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various real-world engineering applications, such as engineering design, industrial manufacturing systems, and water distribution networks, are complex problems. Evolutionary computation is a hot topic of interest amongst researchers in various disciplines of engineering and science. Evolutionary computation is a group of optimization algorithms used for solving global optimization problems, which is inspired by biological evolution. It includes various signal and population-based methods with a meta-heuristic or stochastic optimization part.

In recent years, evolutionary computation methods have been successfully utilized to address complex real-world problems. The literature is abundant with several other approaches that share the same goal: to find a new optimal solution with satisfactory quality by alternating search strategies. Many theoretical and experimental studies have proved significant evolutionary computation properties. The most famous evolutionary computation methods are the genetic algorithm (GA), evolution strategy (ES), differential evolution (DE), particle swarm optimization (PSO), bacterial foraging optimization (BFO), ant colony optimization (ACO), and the memetic algorithm (MA). However, with the fast growth of complex systems, the optimization problems become much larger and complicated. The common issues facing evolutionary algorithms are the dimension of objective functions, decision variables, or constraints.

In light of the expanding interest for new innovative methods of solving real-world and engineering optimization problems, this Special Issue intends to promote high-quality research outputs in the latest progress and improvement of evolutionary algorithms and engineering applications and offers recent advanced research in the field to serve the researchers and practitioners. The main interest is on interdisciplinary research on the evolutionary algorithm, using modern computational intelligence theories, methods, and practices. We invite the researchers to submit their original contributions addressing particular challenging aspects in evolutionary computation from both theoretical and applied viewpoints. Authors are encouraged to submit their contributions covering the following topics:

Methods (but not be limited to):

  • Evolutionary computation
  • Swarm intelligence
  • Meta-heuristics
  • Genetic algorithm
  • Genetic programming
  • Differential evolution
  • Particle swarm optimization
  • Ant colony optimization
  • Bacterial foraging optimization

Applications (but not be limited to):

  • Material Optimization
  • Process Optimization
  • Engineering design problems
  • Complex system modelling and optimization
  • Constraint handling
  • Parameters tuning
  • Industrial problems
  • Benchmarks
  • Imaging and vision
  • Knowledge processing
  • Intelligent fault detection
  • Control and manufacturing applications
  • Multi/Many-objective optimization
  • Real-world applications for complex systems

Prof. Dr. Amir H. Gandomi
Dr. Laith Abualigah
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolutionary computation
  • swarm intelligence
  • meta-heuristics
  • genetic algorithm
  • genetic programming
  • differential evolution
  • particle swarm optimization
  • ant colony optimization
  • bacterial foraging optimization
  • material optimization
  • process optimization
  • engineering design problems
  • complex system modelling and optimization
  • constraint handling
  • parameters tuning
  • industrial problems
  • benchmarks
  • imaging and vision
  • knowledge processing
  • intelligent fault detection
  • control and manufacturing applications
  • multi/many-objective optimization
  • real-world applications for complex systems

Published Papers (13 papers)

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Research

21 pages, 7388 KiB  
Article
Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization
by Wendi Xu, Xianpeng Wang, Qingxin Guo, Xiangman Song, Ren Zhao, Guodong Zhao, Yang Yang, Te Xu and Dakuo He
Processes 2023, 11(3), 693; https://doi.org/10.3390/pr11030693 - 24 Feb 2023
Cited by 2 | Viewed by 1472
Abstract
Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer optimization (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” is continuous and is firstly performed by Professors L. Feng and H.D. [...] Read more.
Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer optimization (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” is continuous and is firstly performed by Professors L. Feng and H.D. Wang, and “4” are firstly proposed by our group for discrete cases. As a new computational paradigm, theoretical insights into SMOs are relatively rare now. Therefore, we present a proposal on the fine brushworks of SMOs for theoretical advances here, which is based on a case study of a permutation flow shop scheduling problem (PFSP) in manufacturing systems via lenses of building blocks, transferring gaps, auxiliary task and asynchronous rhythms. The empirical studies on well-studied benchmarks enrich the rough strokes of SMOs and guide future designs and practices in ETO based manufacturing scheduling, and even ETO based evolutionary processes for engineering optimization in other cases. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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26 pages, 8171 KiB  
Article
An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems
by Saleh Masoud Abdallah Altbawi, Saifulnizam Bin Abdul Khalid, Ahmad Safawi Bin Mokhtar, Hussain Shareef, Nusrat Husain, Ashraf Yahya, Syed Aqeel Haider, Lubna Moin and Rayan Hamza Alsisi
Processes 2023, 11(2), 498; https://doi.org/10.3390/pr11020498 - 7 Feb 2023
Cited by 5 | Viewed by 2684
Abstract
In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding [...] Read more.
In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding inertia weight, fast convergence rate with modified parameters, as well as avoiding the local optima using a novel functional operator (G). These features make it feasible for solving the majority of the nonlinear optimization problems which is quite hard to achieve with the original version of GBO. The effectiveness and scalability of IGBO are evaluated using well-known benchmark functions. Moreover, the performance of the proposed algorithm is statistically analyzed using ANOVA analysis, and Holm–Bonferroni test. In addition, IGBO was assessed by solving well-known real-world problems. The results of benchmark functions show that the IGBO is very competitive, and superior compared to its competitors in finding the optimal solutions with high convergence and coverage. The results of the studied real optimization problems prove the superiority of the proposed algorithm in solving real optimization problems with difficult and indefinite search domains. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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21 pages, 4585 KiB  
Article
Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity
by Yasser A. Ali, Emad Mahrous Awwad, Muna Al-Razgan and Ali Maarouf
Processes 2023, 11(2), 349; https://doi.org/10.3390/pr11020349 - 21 Jan 2023
Cited by 40 | Viewed by 6028
Abstract
For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and [...] Read more.
For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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14 pages, 1045 KiB  
Article
Prediction Model for the Chemical Futures Price Using Improved Genetic Algorithm Based Long Short-Term Memory
by Yachen Lu, Yufan Teng, Qi Zhang and Jiaquan Dai
Processes 2023, 11(1), 238; https://doi.org/10.3390/pr11010238 - 11 Jan 2023
Cited by 3 | Viewed by 1775
Abstract
In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation [...] Read more.
In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA–LSTM model were compared with those of other models. The results showed that the IGA–LSTM model could effectively capture the characteristics and trends of time-series changes. The results showed that the proposed model obtained the minimum values (MSE = 0.00107, RMSE = 0.03268, and MAPE = 0.0691) in the forecasting of futures prices for two types of chemical products, showing excellent forecasting performance. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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46 pages, 11209 KiB  
Article
Dynamic Chaotic Opposition-Based Learning-Driven Hybrid Aquila Optimizer and Artificial Rabbits Optimization Algorithm: Framework and Applications
by Yangwei Wang, Yaning Xiao, Yanling Guo and Jian Li
Processes 2022, 10(12), 2703; https://doi.org/10.3390/pr10122703 - 14 Dec 2022
Cited by 20 | Viewed by 2671
Abstract
Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy and premature convergence when addressing some complex cases due to the insufficient exploitation phase. In [...] Read more.
Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy and premature convergence when addressing some complex cases due to the insufficient exploitation phase. In contrast, ARO possesses very competitive exploitation potential, but its exploration ability needs to be more satisfactory. To ameliorate the above-mentioned limitations in a single algorithm and achieve better overall optimization performance, this paper proposes a novel chaotic opposition-based learning-driven hybrid AO and ARO algorithm called CHAOARO. Firstly, the global exploration phase of AO is combined with the local exploitation phase of ARO to maintain the respective valuable search capabilities. Then, an adaptive switching mechanism (ASM) is designed to better balance the exploration and exploitation procedures. Finally, we introduce the chaotic opposition-based learning (COBL) strategy to avoid the algorithm fall into the local optima. To comprehensively verify the effectiveness and superiority of the proposed work, CHAOARO is compared with the original AO, ARO, and several state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Systematic comparisons demonstrate that CHAOARO can significantly outperform other competitor methods in terms of solution accuracy, convergence speed, and robustness. Furthermore, the promising prospect of CHAOARO in real-world applications is highlighted by resolving five industrial engineering design problems and photovoltaic (PV) model parameter identification problem. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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26 pages, 3522 KiB  
Article
Diversity-Based Evolutionary Population Dynamics: A New Operator for Grey Wolf Optimizer
by Farshad Rezaei, Hamid R. Safavi, Mohamed Abd Elaziz, Laith Abualigah, Seyedali Mirjalili and Amir H. Gandomi
Processes 2022, 10(12), 2615; https://doi.org/10.3390/pr10122615 - 6 Dec 2022
Cited by 3 | Viewed by 2423
Abstract
Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle [...] Read more.
Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB-EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD. In this algorithm, the three most diversified individuals are first identified at each iteration, and then half of the best-fitted individuals are forced to be eliminated and repositioned around these diversified agents with equal probability. This process can free the merged best individuals located in a closed populated region and transfer them to the diversified and, thus, less-densely populated regions in the search space. This approach is frequently employed to make the search agents explore the whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shifted classical benchmark functions as well as 29 test problems included in the CEC2017 test suite, and four constrained engineering problems. The results obtained by the proposal upon implemented on the classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newly proposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). The experiments demonstrate the significant superiority of the proposed algorithm when applied to a majority of the test functions, recommending the application of the proposed EPD operator to any other meta-heuristic whenever decided to ameliorate their performance. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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17 pages, 1488 KiB  
Article
Safety-Risk Assessment for TBM Construction of Hydraulic Tunnel Based on Fuzzy Evidence Reasoning
by Zhixiao Zhang, Bo Wang, Xiangfeng Wang, Yintao He, Hanxu Wang and Shunbo Zhao
Processes 2022, 10(12), 2597; https://doi.org/10.3390/pr10122597 - 5 Dec 2022
Cited by 4 | Viewed by 2584
Abstract
Due to multiple factors influencing the construction safety of TBM hydraulic tunnels, risk assessment is a critical point of a construction management plan to avoid possible risks. In this paper, a safety-risk evaluation index system of TBM construction for hydraulic tunnels is built [...] Read more.
Due to multiple factors influencing the construction safety of TBM hydraulic tunnels, risk assessment is a critical point of a construction management plan to avoid possible risks. In this paper, a safety-risk evaluation index system of TBM construction for hydraulic tunnels is built based on the identification and analysis of possible sources of risk in techniques, geologic, equipment, management, and accidents. Considering the influence of factors such as the experience level and the expertise of decision makers, a combination assignment method of index weights is proposed based on binary semantics. On the basis of a fuzzy normal distribution used as the subordinate function distribution of fuzzy evaluation levels, the subordinate function distribution of fuzzy evaluation levels under multi-level intersection situations is introduced, and a comprehensive evaluation model of safety risks for TBM tunnel construction is built. The validity and practicality of the evaluation model is examined with the combination of a long-distance water conveyance tunnel project. Results show that the construction safety-risk of the TBM hydraulic tunnel project belongs to the middle-high level, and the safety accident risk belongs to the low level. The study provides guidance of evaluation and control of risks for this tunneling construction being successfully completed. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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16 pages, 3443 KiB  
Article
WSN-Based SHM Optimisation Algorithm for Civil Engineering Structures
by Ying Liu
Processes 2022, 10(10), 2113; https://doi.org/10.3390/pr10102113 - 18 Oct 2022
Cited by 1 | Viewed by 1378
Abstract
With the development of economy and the improvement of architectural aesthetics, civil structure buildings show a trend of diversification and complexity, which brings great challenges to the Structural Health Monitoring (SHM) of civil structure buildings. In order to optimise the structural health monitoring [...] Read more.
With the development of economy and the improvement of architectural aesthetics, civil structure buildings show a trend of diversification and complexity, which brings great challenges to the Structural Health Monitoring (SHM) of civil structure buildings. In order to optimise the structural health monitoring effect of civil structures, reduce monitoring costs, and improve the ability of civil structures to deal with risks, a civil structure health monitoring method combining Variational Modal Decomposition (VMD) and the Gated Recurrent Unit (GRU) is proposed. The gated neural network algorithm of modal decomposition is used, and then a wireless sensor network (WSN) civil structure health monitoring model is constructed on this basis. Finally, the application effect of the model is tested and analysed. The results show that the network energy consumption of this model can reach a minimum of 0.05 J, which is 0.05 J less than that of the Gate Recurrent Unit (GRU) model. The minimum loss value is 0.08. Its Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), and Mean Absolute Percent Error (MAPE) are 0.03, 0.04, and 0.06, respectively; the prediction error is the smallest, the overall amplitude difference monitored by the model remains at a low level of less than 0.01, and the changes are closest to the real situation. This shows that the model improves the operation efficiency, improves the accuracy of health monitoring, enhances the adaptability of building structural health monitoring to complex structures, provides a new way for the development of building structural health monitoring technology, and is conducive to enhancing civil structures. The safety and stability of buildings promote the high-quality development of civil and structural buildings. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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15 pages, 3561 KiB  
Article
Integration of Decay Time Analysis and Radiation Measurement for Quantum-Dot-Based Scintillator’s Characterization
by Sujung Min, Kwang-Hoon Ko, Bumkyoung Seo, Changhyun Roh and Sangbum Hong
Processes 2022, 10(10), 1920; https://doi.org/10.3390/pr10101920 - 22 Sep 2022
Viewed by 1522
Abstract
In this study, we demonstrated the process of an integrated apparatus for decay time analysis and gamma radiation measurement with a liquid-scintillator-based cadmium-doped zinc oxide (CZO) nanomaterial. Generally, time-resolved photon counting is an essential analysis method in the field of precision measurement in [...] Read more.
In this study, we demonstrated the process of an integrated apparatus for decay time analysis and gamma radiation measurement with a liquid-scintillator-based cadmium-doped zinc oxide (CZO) nanomaterial. Generally, time-resolved photon counting is an essential analysis method in the field of precision measurement in the quantum domain. Such photon counting equipment requires a pulse laser that can be repeated quickly while having a sharp pulse width of picoseconds or femtoseconds as a light source. Time-correlated single photon counting (TCSPC) equipment, which is currently a commercial product, is inconvenient for recent development research because the scintillator size and shape are limited. Here, neodymium-doped yttrium aluminum garnet (Nd/YAG) laser TCSPC equipment was constructed to analyze the fluorescence characteristics of scintillators having various sizes and shapes. Then, a liquid scintillator added with CZO nanomaterial was prepared and the Nd/YAG laser TCSPC equipment test was performed. As a result of measuring the scintillator using the manufactured Nd/YAG laser TCSPC equipment, the non-CZO liquid scintillator was analyzed at 2.30 ns and the liquid scintillator equipped with CZO-loaded nanomaterial was analyzed at 11.95 ns. It showed an error within 5% when compared with the result of commercial TCSPC equipment. In addition, it was verified that the Nd/YAG laser TCSPC system can sufficiently measure the decay time in nanoseconds (ns). Moreover, it was presented that the Compton edge energy of Cs−137 is 477.3 keV, which hardly generates a photoelectric effect, and Compton scattering mainly occurs. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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28 pages, 12288 KiB  
Article
Performance of a Novel Enhanced Sparrow Search Algorithm for Engineering Design Process: Coverage Optimization in Wireless Sensor Network
by Rui Liu and Yuanbin Mo
Processes 2022, 10(9), 1691; https://doi.org/10.3390/pr10091691 - 25 Aug 2022
Cited by 5 | Viewed by 1645
Abstract
Burgeoning swarm intelligence techniques have been creating a feasible theoretical computational method for the modeling, simulation, and optimization of complex systems. This study aims to increase the coverage of a wireless sensor network (WSN) and puts forward an enhanced version of the sparrow [...] Read more.
Burgeoning swarm intelligence techniques have been creating a feasible theoretical computational method for the modeling, simulation, and optimization of complex systems. This study aims to increase the coverage of a wireless sensor network (WSN) and puts forward an enhanced version of the sparrow search algorithm (SSA) as a processing tool to achieve this optimization. The enhancement of the algorithm covers three aspects. Firstly, the Latin hypercube sampling technique is utilized to generate the initial population to obtain a more uniform distribution in the search space. Secondly, a sine cosine algorithm with adaptive adjustment and the Lévy flight strategy are introduced as new optimization equations to enhance the convergence efficiency of the algorithm. Finally, to optimize the individuals with poor fitness in the population, a novel mutation disturbance mechanism is introduced at the end of each iteration. Through numerical tests of 13 benchmark functions, the experimental results show that the proposed enhanced algorithm can converge to the optimum faster and has a more stable average value, reflecting its advantages in convergence speed, robustness, and anti-local extremum ability. For the WSN coverage problem, this paper established a current optimization framework based on the swarm intelligence algorithms, and further investigated the performance of nine algorithms applied to the process. The simulation results indicate that the proposed method achieves the highest coverage rate of 97.66% (on average) among the nine algorithms in the calculation cases, which is increased by 13.00% compared with the original sparrow search algorithm and outperforms other methods by 1.47% to 15.34%. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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14 pages, 2399 KiB  
Article
A Comparison of Three Different Group Intelligence Algorithms for Hyperspectral Imagery Classification
by Yong Wang and Weibo Zeng
Processes 2022, 10(9), 1672; https://doi.org/10.3390/pr10091672 - 23 Aug 2022
Cited by 2 | Viewed by 1382
Abstract
The classification effect of hyperspectral remote sensing images is greatly affected by the problem of dimensionality. Feature extraction, as a common dimension reduction method, can make up for the deficiency of the classification of hyperspectral remote sensing images. However, different feature extraction methods [...] Read more.
The classification effect of hyperspectral remote sensing images is greatly affected by the problem of dimensionality. Feature extraction, as a common dimension reduction method, can make up for the deficiency of the classification of hyperspectral remote sensing images. However, different feature extraction methods and classification methods adapt to different conditions and lack comprehensive comparative analysis. Therefore, principal component analysis (PCA), linear discriminant analysis (LDA), and locality preserving projections (LPP) were selected to reduce the dimensionality of hyperspectral remote sensing images, and subsequently, support vector machine (SVM), random forest (RF), and the k-nearest neighbor (KNN) were used to classify the output images, respectively. In the experiment, two hyperspectral remote sensing data groups were used to evaluate the nine combination methods. The experimental results show that the classification effect of the combination method when applying principal component analysis and support vector machine is better than the other eight combination methods. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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16 pages, 3201 KiB  
Article
A Study Using Optimized LSSVR for Real-Time Fault Detection of Liquid Rocket Engine
by Peihao Huang, Huahuang Yu and Tao Wang
Processes 2022, 10(8), 1643; https://doi.org/10.3390/pr10081643 - 18 Aug 2022
Cited by 4 | Viewed by 1686
Abstract
Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this [...] Read more.
Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this paper proposed a real-time fault detection approach using a genetic algorithm-based least squares support vector regression (GA-LSSVR) algorithm for the real-time fault detection of the rocket engine. In order to obtain effective training samples, the data is normalized in this paper. Then, the GA-LSSVR algorithm is derived through comprehensive considerations of the advantages of the Support Vector Regression (SVR) algorithm and Least Square Support Vector Regression (LSSVR). What is more, this paper provided the genetic algorithm to search for the optimal LSSVR parameters. In the end, the computational results of the suggested approach using the rocket practical experimental data are given out. Through the analysis of the results, the effectiveness and the detection accuracy of this presented real-time fault detection method using LSSVR GA-optimized is verified. The experiment results show that this method can effectively diagnose this hydrogen-oxygen rocket engine in real-time, and the method has engineering application value. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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28 pages, 9820 KiB  
Article
Modeling and Optimization of Assembly Line Balancing Type 2 and E (SLBP-2E) for a Reconfigurable Manufacturing System
by Abdul Salam Khan, Razaullah Khan, Waqas Saleem, Bashir Salah and Soliman Alkhatib
Processes 2022, 10(8), 1582; https://doi.org/10.3390/pr10081582 - 12 Aug 2022
Cited by 2 | Viewed by 2244
Abstract
This study undertakes the line balancing problem while allocating reconfigurable machines to different workstations. A multi-objective model is used to analyze the position of workstations, assignment of configurations to workstations, and operation scheduling in a reconfigurable manufacturing environment. A model is presented that [...] Read more.
This study undertakes the line balancing problem while allocating reconfigurable machines to different workstations. A multi-objective model is used to analyze the position of workstations, assignment of configurations to workstations, and operation scheduling in a reconfigurable manufacturing environment. A model is presented that comprises the objectives of the Total Time (TT), the Line Efficiency Index (LEI), and the Customer Satisfaction Index (CSI). The objective is to minimize the completion time and maximize the efficiency of a production line. The proposed model combines the Simple Line Balancing Problems Type 2 and Type E in the form of SLBP-2E. The presented problems are addressed by using a heuristic solution approach due to non-polynomial hard formulation. The heuristic approach is designed to assess different solutions based on no repositioning, separate repositioning of workstations and configuration, and simultaneous repositioning of workstations and configurations. A detailed assessment is presented regarding the efficiency as well as the effectiveness of proposed approaches. Finally, conclusions and future research avenues are outlined. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization (II))
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