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A modified artificial electric field algorithm and its application

PHYSICA SCRIPTA 2024年第12期99卷 125273-125273页

作者： Lin, Qiuhong Zhang, Lieping Cheng, Jiatang Guilin Univ Technol Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin 541006 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin 541006 Peoples R China Guilin Univ Aerosp Technol Sch Mech Engn Guilin Peoples R China

As an efficient meta-heuristic technique, artificial electricfield algorithm(AEFA)has been extensivelyapplied to tackle various challenging tasks posed by practical scenarios. However, in the classicalAEFA, thefitness function has a cumulative effect on the charge, resulting in limited search *** address this issue, a modified AEFA(MAEFA)is presented in this paper. More specifically, a novelcharge calculation scheme is introduced to overcome the cumulative effect by gradually distinguishingthe charges of particles during the evolutionary process. Further, an alternating search strategy isdeveloped to calculate the total electrostatic force, thereby reinforcing the guiding effect of excellentindividuals on the entire population. Subsequently, the performance of MAEFA is investigated using42 well-benchmarked functions, two chaotic time series prediction problems, and two engineeringdesign problems. Experimental results reveal that MAEFA is more competitive in comparison withseveral established AEFAs and 20 popular meta-heuristic techniques.

关键词： artificial electric field algorithm charge calculation scheme alternating search strategy chaotic time series prediction engineering design problems

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artificial electric field algorithm with repulsion mechanism

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EXPERT SYSTEMS 2024年第12期41卷

作者： Zhang, Gengfei Cheng, Jiatang Guilin Univ Technol Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin 541006 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin Peoples R China

Due to its outstanding performance in addressing optimization problems, artificial electric field (AEF) algorithm has garnered increasing notice in recent years. Nevertheless, numerous studies indicate that AEF is susceptible to premature convergence when the region influenced by the global optimum constitutes a small fraction of the entire solution space. By conducting micro-level research on the particles during the evolution process of AEF, it is revealed that the primary factors influencing optimization performance are the Coulomb's electrostatic force mechanism and the fixed attenuation factor. Inspired by this observation, we propose an improved version named artificial electric field algorithm with repulsion mechanism (RMAEF). Specifically, in RMAEF, a repulsion mechanism is incorporated to make particles escape from local optima. Furthermore, an adaptive attenuation factor is employed to update dynamically Coulomb's constant. RMAEF is compared with AEF and its state-of-art variants under 44 test functions from CEC 2005 and CEC 2014 test suites. From the experiment results, it is obvious that among 14 benchmark functions from CEC 2005 on 30D and 50D optimization, the RMAEF algorithm exhibits superior performance on 8 and 9 functions compared with advanced variants of AEF. For CEC 2014 on 30D and 50D optimization, the RMAEF algorithm produces the best results on 11 and 12 functions, respectively. In addition, three real-world problems are also used to verify the versatility and robustness. The results demonstrate that RMAEF outperforms its competitors in terms of overall performance.

关键词： artificial electric field algorithm attenuation factor optimization repulsion mechanism

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Optimal Selection of Features Using artificial electric field algorithm for Classification

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8355-8369页

作者： Das, Himansu Naik, Bighnaraj Behera, H. S. Veer Surendra Sai Univ Technol Dept Informat Technol Sambalpur 768018 Odisha India Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Odisha India

The high-dimensional features in the data may affect the performance of the classification model as all of them are not useful. The selection of relevant optimal features is a tedious task, especially the data in which the number of features is high. This paper proposed a new feature selection (FS) approach based on the artificial electric field algorithm (AEFA) called FSAEFA, to select the most suitable optimal features for classification. The AEFA is an efficient and effective population-based optimization technique inspired by the principle of Coulomb's law of electrostatic force (CLEF) to solve optimization problems. The proposed FSAEFA approach can search the most suited subset of features by attracting the worst individual features towards the best individual features by using the attraction of CLEF. The proposed FSAEFA approach has been evaluated and compared with some other FS approaches on ten publicly available benchmark datasets. The experimental result has been indicated that the performance of the proposed method is superior over the existing methods in most of the cases. The significance of the proposed FS approach along with their counter parts have been statistically measured and compared by using Friedman test and Holm procedure. It has been determined that the proposed FSAEFA approach is found to be more efficient than the existing FS approaches.

关键词： artificial electric field algorithm Classification Optimization Feature selection Wrapper method

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Opposition-based artificial electric field algorithm and its application to FOPID controller design for unstable magnetic ball suspension system

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2021年第2期24卷 469-479页

作者： Demiroren, Aysen Ekinci, Serdar Hekimoglu, Baran Izci, Davut Istanbul Tech Univ Dept Elect Engn TR-34469 Istanbul Turkey Batman Univ Dept Comp Engn TR-72100 Batman Turkey Batman Univ Dept Elect & Elect Engn TR-72100 Batman Turkey Batman Univ Vocat Sch Tech Sci TR-72060 Batman Turkey

In this study, an improved version of artificial electric field (AEF) algorithm, named as opposition-based AEF (ObAEF), has been proposed for the first time ever to tune a fractional order PID (FOPID) controller used in a magnetic ball suspension system. The basic AEF algorithm is a novel physics-inspired, population-based meta-heuristic optimization method that mathematically mimics Coulomb's electrostatic force between charged particles. The proposed ObAEF algorithm is the improved version of the AEF which utilizes the opposition-based learning strategy to enhance the AEF algorithm's exploration capability. To validate the performance, the novel ObAEF algorithm was applied to 6 well-known benchmark optimization problems of Sphere, Rosenbrock, Schwefel, Ackley, Egg Crate and Easom. The results were also compared with other algorithms such as basic AEF, atom search optimization (ASO) and artificial bee colony (ABC). It was also used to tune FOPID controller (ObAEF-FOPID) to improve the transient response of a magnetic levitation (maglev) system by minimizing a new objective function having a simple structure. The latter was proposed to minimize the maximum overshoot, settling and rise times along with steady state error of magnetically suspended ball's position. The convergence profile and statistical analyzes were conducted to illustrate the success of the proposed algorithm. The effectiveness and superiority of the ObAEF-FOPID controller was further investigated through frequency response analysis and again compared with AEF, ABC and ASO based FOPID controllers as in statistical success, convergence profile and transient analyses. The results showed that the proposed ObAEF-FOPID has better control performance than those tuned by AEF, ASO and ABC algorithms. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

关键词： artificial electric field algorithm Optimization Magnetic ball suspension system Magnetic levitation (maglev) Fractional order PID controller Opposition-based learning

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Binary artificial electric field algorithm

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1155-1183页

作者： Chauhan, Dikshit Yadav, Anupam Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

artificial electric field algorithm (AEFA) is one of the recent population-based optimization techniques, it is inspired by the electrostatic force theory. This article aims to design a novel binary version of the AEFA scheme to improve the performance of the original AEFA scheme in discrete space. The popular S-shaped and V-shaped functions are used to design the binary versions of the AEFA. The efficiency and the optimization ability of the proposed binary versions of the AEFA are studied theoretically as well as experimentally. An extensive experimental study is performed to understand the performance of the proposed schemes. A set of 24 benchmark problems are solved using binary versions of AEFA the experimental results are compared with nine state-of-the-art algorithms. The running time complexity and Wilcoxon's signed-rank statistical test are also conducted to judge the proposed algorithms. In addition to the experimental studies, theoretical analysis is also carried out which suggests the convergence scenario of the proposed schemes. These studies suggest that the designed binary versions of the AEFA are very efficient and competent in addressing difficult optimization problems.

关键词： Meta heuristic algorithms Optimization artificial electric field algorithm Benchmark problems Binary algorithm S-shaped function V-shaped function

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Improved artificial electric field algorithm Using Nelder-Mead Simplex Method for Optimization Problems 4

Improved Artificial Electric Field Algorithm Using Nelder-Me...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Izci, Davut Ekinci, Serdar Orenc, Sedat Demiroren, Aysen Batman University Vocational School of Technical Sciences Batman Turkey Batman University Department of Computer Engineering Batman Turkey Batman University Department of Electrical and Electronic Engineering Batman Turkey Istanbul Technical University Department of Electrical Engineering Istanbul Turkey

ISBN: (数字)9781728190907

ISBN: (纸本)9781728190907

artificial electric field (AEF) algorithm was developed to be an alternative method for tackling with real-world engineering problems as a physics inspired meta-heuristic algorithm. Due to its stochastic nature, AEF suffers from poor exploitation, which needs to be improved. Therefore, this study attempts to obtain an effective structure by using AEF together with Nelder-Mead (NM) simplex search method. The proposed method has been named as artificial electric field with Nelder-Mead algorithm (AEF-NM). The proposed algorithm performs global search via AEF whereas NM is used to achieve better local search ability. In this way, a better performing algorithm has been achieved. Four well-known benchmark functions (Rosenbrock, Ackley, Sphere, Schwefel) were adopted to test the proposed algorithm. Comparative statistical analyses were carried out using the state-of-the-art algorithms such as sinecosine, atom search optimization, salp swarm and original AEF algorithms to observe the capability of the proposed AEF-NM. The proposed algorithm was shown to be have better performance than other compared algorithms. © 2020 IEEE.

关键词： artificial electric field algorithm Nelder-Mead algorithm hybrid algorithm meta-heuristic algorithms

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Hybrid artificial electric field algorithm for assembly line balancing problem with equipment model selection possibility

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KNOWLEDGE-BASED SYSTEMS 2021年 219卷 106905-106905页

作者： Niroomand, Sadegh Firouzabad Inst Higher Educ Dept Ind Engn Firouzabad Fars Iran

Existence of different models of equipment of the tasks of an assembly line has two major direct effects on the task operational times, and the equipment purchasing cost. This issue is reflected for the first time in this study in a straight assembly line balancing problem with single model product. The problem is formulated first as a non-linear model and then is linearized. Because of the NP-hard nature of the problem some meta-heuristic algorithms are proposed to solve it efficiently. For this aim a complete encoding-decoding scheme and the artificial electric field algorithm as a most recent algorithm of the literature and also the simulated annealing algorithm are considered. In order to fit to the problem of this study, some modified and hybrid versions of these algorithms are proposed and are tuned by the Taguchi experimental design method. A very deep computational experiments using 24 modified test problems of the literature show the superiority of the artificial electric field algorithm when is hybridized by the simulated annealing algorithm and a local search phase. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Assembly line balancing Meta-heuristic artificial electric field algorithm Simulated annealing Local search Hybridization

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An archive-based self-adaptive artificial electric field algorithm with orthogonal initialization for real-parameter optimization problems

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Chauhan, Dikshit Yadav, Anupam Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

In this article, a series of learning strategies are proposed to enhance the optimization ability of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool that can greatly influence the adaptability of population-based optimization algorithms. This article proposes, (i) an orthogonal array based learning strategy to generate a better initial population for the artificial electric field algorithm. Along with the changes in the initialization mechanism, this article also proposes, (ii) an archive-based self-adaptive learning strategy for an artificial electric field algorithm. The proposed learning strategy divides the population into ordinary and extraordinary sub-populations, each with distinct learning mechanisms. The ordinary sub population utilizes six learning strategies based on three archives, which contain individuals of different quality levels. We incorporate, (iii) a mutation strategy also to update the extraordinary sub-population. Finally, (iv) a self-adaptive strategy is implemented to dynamically adjust the parameters of the proposed algorithm. The effectiveness of these mechanisms is assessed through an extensive analysis of exploration-exploitation dynamics and diversity. Furthermore, an independent structural study is conducted to examine the impact of implemented mechanisms on the algorithm's behavior and efficiency. The proposed algorithm is evaluated on real parameter CEC 2017 problems across different dimensional search spaces. It is compared to eleven state-of-the-art algorithms, and the results demonstrate superior performance in terms of solution accuracy, convergence rate, search capability, and stability. The overall ranking highlights its exceptional potential for solving challenging optimization problems. Additionally, it outperforms other state-of-the-art algorithms across various dimensions, achieving accuracy rates of 64.48%, 70.05%, 78.73%, and 79.25% for dimensions 10, 30, 50, and 100, respect

关键词： Metaheuristic optimization Experimental design method artificial electric field algorithm Self-adaptive Elite archive Mutation operator

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Discrete artificial electric field algorithm for high-order graph matching

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APPLIED SOFT COMPUTING 2020年 92卷 106260-106260页

作者： Anita Yadav, Anupam Natl Inst Technol Uttarakhand Dept Math Srinagar 246174 Uttarakhand India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

High-order graph matching is a problem of establishing the correspondences between two sets of visual features subject to high-order matching constraints. This is an NP-hard combinatorial optimization problem and formulated as a maximization problem of matching score over all permutations of features. artificial electric field algorithm (AEFA) (Yadav et al., 2019) is a proven optimization algorithm in the family of meta-heuristic and performed well for continuous optimization problems. In this article, we extended the AEFA algorithm for combinatorial high-order graph matching problems and introduced a discrete artificial electric field algorithm (DAEFA). This framework incorporates the redefine position and velocity representation scheme, addition-subtraction operation, velocity and position update rules, and a problem specific initialization by using heuristic information. The efficiency of the proposed algorithm is tested over three well-known datasets: synthetic, CMU house and real-world datasets. The computational results measured the matching score, accuracy of matching and established the correspondences between two graphs. The computational results show the outperformance of the proposed algorithm over the other state-of-art algorithms in terms of good matching score and accuracy both. (C) 2020 Elsevier B.V. All rights reserved.

关键词： High order graph matching artificial intelligence artificial electric field algorithm

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An improved artificial electric field algorithm and its application in neural network optimization

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COMPUTERS & electricAL ENGINEERING 2022年 101卷

作者： Cheng, Jiatang Xu, Peizhen Xiong, Yan Guilin Univ Technol Coll Mech & Control Engn Guilin 541006 Peoples R China

artificial electric field (AEF) algorithm is a newly developed meta-heuristic optimization technique inspired by the Coulomb's law of electrostatic force. As so far, AEF algorithm has been successfully applied in some scientific research and engineering fields, but it still has the defects of premature convergence and poor search ability in handling complex optimization problems. To address such issues, a novel Coulomb's constant generation scheme is explored to calculate the electrostatic force, and then an improved AEF (IAEF) algorithm is developed to strengthen the global exploration capability. Subsequently, 18 popular test functions with different dimensions are selected as benchmarks for performance evaluation. Furthermore, two nonlinear problems of neural network optimization are employed to further investigate the effectiveness and superiority of IAEF algorithm. Experimental results demonstrate that the proposed IAEF is efficient and effective optimization method in comparison with the conventional AEF and several famous evolutionary algorithms.

关键词： artificial electric field algorithm Coulomb's constant Optimization Neural network

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