Editorial

6 pages, 358 KiB

Open AccessEditorial

Evolutionary Computation: Theories, Techniques, and Applications

by Vincent A. Cicirello

Appl. Sci. 2024, 14(6), 2542; https://doi.org/10.3390/app14062542 - 18 Mar 2024

Viewed by 495

Evolutionary computation is now nearly 50 years old, originating with the seminal work of John Holland at the University of Michigan in 1975 which introduced the genetic algorithm [...] Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

Research

Jump to: Editorial

30 pages, 9700 KiB

Open AccessArticle

Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II

by Hamza Reffad and Adel Alti

Appl. Sci. 2023, 13(8), 5218; https://doi.org/10.3390/app13085218 - 21 Apr 2023

Viewed by 1548

Abstract

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, [...] Read more.

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, such as manufacturing, predictive maintenance, smart logistics, and energy networks. This has been assisted by smart devices and intelligent machines that aim to leverage flexible smart Enterprise Resource Planning (ERP) regarding all the needs of the company. Many emerging research approaches are still in progress with the view to composing IoT and Cloud services for meeting the expectation of companies. Many of these approaches use ontologies and metaheuristics to optimize service quality of composite IoT and Cloud services. These approaches lack responsiveness to changing customer needs as well as changes in the power capacity of IoT devices. This means that optimization approaches need an effective adaptive strategy that replaces one or more services with another at runtime, which improves system performance and reduces energy consumption. The idea is to have a system that optimizes the selection and composition of services to meet both service quality and energy saving by constantly reacting to context changes. In this paper, we present a semantic dynamic cooperative service selection and composition approach while maximizing customer non-functional needs and quickly selecting the relevant service drive with energy saving. Particularly, we introduce a new QoS energy violation degree with a cooperative energy-saving mechanism to ensure application durability while different IoT devices are run-out of energy. We conduct experiments on a real business process of the company SETIF IRIS using different cooperative strategies. Experimental results showed that the smart ERP system with the proposed approach achieved optimized ERP performance in terms of average service quality and average energy consumption ratio equal to 0.985 and 0.057, respectively, in all simulated configurations compared to ring and maser/slave methods. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

19 pages, 5312 KiB

Open AccessArticle

Multi-Objective Optimization of Electric Vehicle Charging Station Deployment Using Genetic Algorithms

by Vasiliki Lazari and Athanasios Chassiakos

Appl. Sci. 2023, 13(8), 4867; https://doi.org/10.3390/app13084867 - 13 Apr 2023

Cited by 6 | Viewed by 3513

Abstract

The incorporation of electric vehicles into the transportation system is imperative in order to mitigate the environmental impact of fossil fuel use. This requires establishing methods for deploying the charging infrastructure in an optimal way. In this paper, an optimization model is developed [...] Read more.

The incorporation of electric vehicles into the transportation system is imperative in order to mitigate the environmental impact of fossil fuel use. This requires establishing methods for deploying the charging infrastructure in an optimal way. In this paper, an optimization model is developed to identify both the number of stations to be deployed and their respective locations that minimize the total cost by utilizing Genetic Algorithms. This is implemented by combining these components into a linear objective function aiming to minimize the overall cost of deploying the charging network and maximize service quality to users by minimizing the average travel distance between demand spots and stations. Several numerical and practical considerations have been analyzed to provide an in-depth study and a deeper understanding of the model’s capabilities. The optimization is done through commercial software that is appropriately parametrized to adjust to the specific problem. The model is simple yet effective in solving a variety of problem structures, optimization goals and constraints. Further, the quality of the solution seems to be marginally affected by the shape and size of the problem area, as well as the number of demand spots, and this may be considered one of the strengths of the algorithm. The model responds expectedly to variations in the charging demand levels and can effectively run at different levels of grid discretization. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

21 pages, 2191 KiB

Open AccessArticle

Genetic Algorithms Optimized Adaptive Wireless Network Deployment

by Rahul Dubey and Sushil J. Louis

Appl. Sci. 2023, 13(8), 4858; https://doi.org/10.3390/app13084858 - 12 Apr 2023

Cited by 1 | Viewed by 1050

Abstract

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case [...] Read more.

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by

5.62 %

to

121.73 %

. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

23 pages, 1498 KiB

Open AccessArticle

Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation

by Weidong Wu, Xiaoyan Sun, Guangyi Man, Shuai Li and Lin Bao

Appl. Sci. 2023, 13(4), 2243; https://doi.org/10.3390/app13042243 - 09 Feb 2023

Cited by 1 | Viewed by 1017

Abstract

Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated [...] Read more.

Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view optimization for personalized recommendation. Firstly, multidimensional preference user surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are constructed to approximate the different perceptions of preferences and serve for population evolution. Next, a modified multifactorial evolutionary algorithm is used for the first time in the IEAs domain to recommend diverse and novel items for multiple preferences. It includes initialization and diversification management of a population with skill factors, recommendation lists of preference grading and interactive model management of inheriting previous information. Comprehensive comparison studies in the Amazon dataset show that the proposed models and algorithm facilitate the mining of knowledge between preferences. Eventually, at the cost of losing only about 5% of the Hit Ratio and Average Precision, the Individual Diversity is improved by 54.02%, the Self-system Diversity by 3.7%, the Surprise Degree by 2.69%, and the Preference Mining Degree by 16.05%. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

23 pages, 10215 KiB

Open AccessArticle

Estimation of Interaction Locations in Super Cryogenic Dark Matter Search Detectors Using Genetic Programming-Symbolic Regression Method

by Nikola Anđelić, Sandi Baressi Šegota, Matko Glučina and Zlatan Car

Appl. Sci. 2023, 13(4), 2059; https://doi.org/10.3390/app13042059 - 05 Feb 2023

Cited by 2 | Viewed by 1005

Abstract

The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs)—candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector; however, there are many other interactions (background interactions). To separate [...] Read more.

The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs)—candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector; however, there are many other interactions (background interactions). To separate background interactions from the signal, it is necessary to measure the interaction energy and to reconstruct the location of the interaction between WIMPs and the nuclei. In recent years, some research papers have been investigating the reconstruction of interaction locations using artificial intelligence (AI) methods. In this paper, a genetic programming-symbolic regression (GPSR), with randomly tuned hyperparameters cross-validated via a five-fold procedure, was applied to the SuperCDMS experiment to estimate the interaction locations with high accuracy. To measure the estimation accuracy of obtaining the SEs, the mean and standard deviation (

σ

) values of

R^{2}

, the root-mean-squared error (

R M S E

), and finally, the mean absolute error (

M A E

) were used. The investigation showed that using GPSR, SEs can be obtained that estimatethe interaction locations with high accuracy. To improve the solution, the five best SEs were combined from the three best cases. The results demonstrated that a very high estimation accuracy can be achieved with the proposed methodology. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

23 pages, 632 KiB

Open AccessArticle

Random Orthogonal Search with Triangular and Quadratic Distributions (TROS and QROS): Parameterless Algorithms for Global Optimization

by Bruce Kwong-Bun Tong, Chi Wan Sung and Wing Shing Wong

Appl. Sci. 2023, 13(3), 1391; https://doi.org/10.3390/app13031391 - 20 Jan 2023

Cited by 1 | Viewed by 1032

Abstract

In this paper, the behavior and performance of Pure Random Orthogonal Search (PROS), a parameter-free evolutionary algorithm (EA) that outperforms many existing EAs on the well-known benchmark functions with finite-time budget, are analyzed. The sufficient conditions to converge to the global optimum are [...] Read more.

In this paper, the behavior and performance of Pure Random Orthogonal Search (PROS), a parameter-free evolutionary algorithm (EA) that outperforms many existing EAs on the well-known benchmark functions with finite-time budget, are analyzed. The sufficient conditions to converge to the global optimum are also determined. In addition, we propose two modifications to PROS, namely Triangular-Distributed Random Orthogonal Search (TROS) and Quadratic-Distributed Random Orthogonal Search (QROS). With our local search mechanism, both modified algorithms improve the convergence rates and the errors of the obtained solutions significantly on the benchmark functions while preserving the advantages of PROS: parameterless, excellent computational efficiency, ease of applying to all kinds of applications, and high performance with finite-time search budget. The experimental results show that both TROS and QROS are competitive in comparison to several classic metaheuristic optimization algorithms. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

19 pages, 11427 KiB

Open AccessArticle

Optimization on Linkage System for Vehicle Wipers by the Method of Differential Evolution

by Tsai-Jung Chen, Ying-Ji Hong, Chia-Han Lin and Jing-Yuan Wang

Appl. Sci. 2023, 13(1), 332; https://doi.org/10.3390/app13010332 - 27 Dec 2022

Viewed by 1265

Abstract

We consider an optimization problem on the maximal magnitude of angular acceleration of the output-links of a commercially available center-driven linkage system (CDLS) for vehicle wipers on windshield. The purpose of this optimization is to improve the steadiness of a linkage system without [...] Read more.

We consider an optimization problem on the maximal magnitude of angular acceleration of the output-links of a commercially available center-driven linkage system (CDLS) for vehicle wipers on windshield. The purpose of this optimization is to improve the steadiness of a linkage system without weakening its normal function. Thus this optimization problem is considered under the assumptions that the frame of the fixed links of linkage system is unchanged and that the input-link rotates at the same constant angular speed with its length unchanged. To meet the usual requirements for vehicle wipers on windshield, this optimization problem must be solved subject to 10 specific constraints. We expect that optimizing the maximal magnitude of angular acceleration of the output-links of a linkage system would also be helpful for reducing the amplitudes of sound waves of wiper noise. We establish the motion model of CDLS and then justify this model with ADAMS. We use a “Differential Evolution” type method to search for the minimum of an objective function subject to 10 constraints for this optimization problem. Our optimization computation shows that the maximal magnitude of angular acceleration of both output-links of this linkage system can be reduced by more than 10%. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

17 pages, 320 KiB

Open AccessArticle

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

by Shir Li Wang, Sarah Hazwani Adnan, Haidi Ibrahim, Theam Foo Ng and Parvathy Rajendran

Appl. Sci. 2022, 12(22), 11367; https://doi.org/10.3390/app122211367 - 09 Nov 2022

Cited by 1 | Viewed by 978

Abstract

Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm [...] Read more.

Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

19 pages, 3203 KiB

Open AccessArticle

Hybrid Discrete Particle Swarm Optimization Algorithm with Genetic Operators for Target Coverage Problem in Directional Wireless Sensor Networks

by Yu-An Fan and Chiu-Kuo Liang

Appl. Sci. 2022, 12(17), 8503; https://doi.org/10.3390/app12178503 - 25 Aug 2022

Cited by 7 | Viewed by 1301

Abstract

For a sensing network comprising multiple directional sensors, maximizing the number of covered targets but minimizing sensor energy use is a challenging problem. Directional sensors that can rotate to modify their sensing directions can be used to increase coverage and decrease the number [...] Read more.

For a sensing network comprising multiple directional sensors, maximizing the number of covered targets but minimizing sensor energy use is a challenging problem. Directional sensors that can rotate to modify their sensing directions can be used to increase coverage and decrease the number of activated sensors. Solving this target coverage problem requires creating an optimized schedule where (1) the number of covered targets is maximized and (2) the number of activated directional sensors is minimized. Herein, we used a discrete particle swarm optimization algorithm (DPSO) combined with genetic operators of the genetic algorithm (GA) to compute feasible and quasioptimal schedules for directional sensors and to determine the sensing orientations among the directional sensors. We simulated the hybrid DPSO with GA operators and compared its performance to a conventional greedy algorithm and two evolutionary algorithms, GA and DPSO. Our findings show that the hybrid scheme outperforms the greedy, GA, and DPSO algorithms up to 45%, 5%, and 9%, respectively, in terms of maximization of covered targets and minimization of active sensors under different perspectives. Finally, the simulation results revealed that the hybrid DPSO with GA produced schedules and orientations consistently superior to those produced when only DPSO was used, those produced when only GA was used, and those produced when the conventional greedy algorithm was used. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

28 pages, 1039 KiB

Open AccessArticle

Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selection

by Daniel Parra, Alberto Gutiérrez-Gallego, Oscar Garnica, Jose Manuel Velasco, Khaoula Zekri-Nechar, José J. Zamorano-León, Natalia de las Heras and J. Ignacio Hidalgo

Appl. Sci. 2022, 12(16), 8251; https://doi.org/10.3390/app12168251 - 18 Aug 2022

Cited by 1 | Viewed by 1470

Abstract

In this paper, we experimented with a set of machine-learning classifiers for predicting the risk of a person being overweight or obese, taking into account his/her dietary habits and socioeconomic information. We investigate with ten different machine-learning algorithms combined with four feature-selection strategies [...] Read more.

In this paper, we experimented with a set of machine-learning classifiers for predicting the risk of a person being overweight or obese, taking into account his/her dietary habits and socioeconomic information. We investigate with ten different machine-learning algorithms combined with four feature-selection strategies (two evolutionary feature-selection methods, one feature selection from the literature, and no feature selection). We tackle the problem under a binary classification approach with evolutionary feature selection. In particular, we use a genetic algorithm to select the set of variables (features) that optimize the accuracy of the classifiers. As an additional contribution, we designed a variant of the Stud GA, a particular structure of the selection operator of individuals where a reduced set of elitist solutions dominate the process. The genetic algorithm uses a direct binary encoding, allowing a more efficient evaluation of the individuals. We use a dataset with information from more than 1170 people in the Spanish Region of Madrid. Both evolutionary and classical feature-selection methods were successfully applied to Gradient Boosting and Decision Tree algorithms, reaching values up to 79% and increasing the average accuracy by two points, respectively. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

19 pages, 2488 KiB

Open AccessArticle

Optimizing the Layout of Run-of-River Powerplants Using Cubic Hermite Splines and Genetic Algorithms

by Alejandro Tapia Córdoba, Pablo Millán Gata and Daniel Gutiérrez Reina

Appl. Sci. 2022, 12(16), 8133; https://doi.org/10.3390/app12168133 - 14 Aug 2022

Cited by 1 | Viewed by 1294

Abstract

Despite the clear advantages of mini hydropower technology to provide energy access in remote areas of developing countries, the lack of resources and technical training in these contexts usually lead to suboptimal installations that do not exploit the full potential of the environment. [...] Read more.

Despite the clear advantages of mini hydropower technology to provide energy access in remote areas of developing countries, the lack of resources and technical training in these contexts usually lead to suboptimal installations that do not exploit the full potential of the environment. To address this drawback, the present work proposes a novel method to optimize the design of mini-hydropower plants with a robust and efficient formulation. The approach does not involve typical 2D simplifications of the terrain penstock layout. On the contrary, the problem is formulated considering arbitrary three-dimensional terrain profiles and realistic penstock layouts taking into account the bending effect. To this end, the plant layout is modeled on a continuous basis through the cubic Hermite interpolation of a set of key points, and the optimization problem is addressed using a genetic algorithm with tailored generation, mutation and crossover operators, especially designed to improve both the exploration and intensification. The approach is successfully applied to a real-case scenario with real topographic data, demonstrating its capability of providing optimal solutions while dealing with arbitrary terrain topography. Finally, a comparison with a previous discrete approach demonstrated that this algorithm can lead to a noticeable cost reduction for the problem studied. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

19 pages, 594 KiB

Open AccessArticle

A Stigmergy-Based Differential Evolution

by Valentín Osuna-Enciso and Elizabeth Guevara-Martínez

Appl. Sci. 2022, 12(12), 6093; https://doi.org/10.3390/app12126093 - 15 Jun 2022

Viewed by 1755

Abstract

Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although [...] Read more.

Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the algorithm’s exploitation process, it is not completely in agreement with the swarms found in nature, where communication among individuals is not centralized. This paper proposes the use of stigmergy as an inspiration to modify the original DE operators to simulate a decentralized information exchange, thus avoiding the application of a global best. The Stigmergy-based DE (SDE) approach was tested on a set of benchmark problems to compare its performance with DE. Even though the execution times of DE and SDE are very similar, our proposal has a slight advantage in most of the functions and can converge in fewer iterations in some cases, but its main feature is the capability to maintain a good convergence behavior as the dimensionality grows, so it can be a good alternative to solve complex problems. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

26 pages, 533 KiB

Open AccessArticle

Cycle Mutation: Evolving Permutations via Cycle Induction

by Vincent A. Cicirello

Appl. Sci. 2022, 12(11), 5506; https://doi.org/10.3390/app12115506 - 29 May 2022

Cited by 3 | Viewed by 1659

Abstract

Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems such as the traveling salesperson problem (TSP), as well as assignment problems such as the quadratic assignment problem (QAP) and largest [...] Read more.

Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems such as the traveling salesperson problem (TSP), as well as assignment problems such as the quadratic assignment problem (QAP) and largest common subgraph (LCS). We propose cycle mutation, a new mutation operator whose inspiration is the well-known cycle crossover operator, and the concept of a permutation cycle. We use fitness landscape analysis to explore the problem characteristics for which cycle mutation works best. As a prerequisite, we develop new permutation distance measures: cycle distance, k-cycle distance, and cycle edit distance. The fitness landscape analysis predicts that cycle mutation is better suited for assignment and mapping problems than it is for ordering problems. We experimentally validate these findings showing cycle mutation’s strengths on problems such as QAP and LCS, and its limitations on problems such as the TSP, while also showing that it is less prone to local optima than commonly used alternatives. We integrate cycle mutation into the open source Chips-n-Salsa library, and the new distance metrics into the open source JavaPermutationTools library. Full article

(This article belongs to the Special Issue Evolutionary Computation: Theories, Techniques, and Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Evolutionary Computation: Theories, Techniques, and Applications

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (14 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI