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artificial electric field algorithm with dynamic neighborhood learning for global optimization

PHYSICA SCRIPTA 2025年第4期100卷 046005-046005页

作者： Lu, Qiuli Cheng, Jiatang Lin, Qiuhong 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

artificial electric field algorithm (AEFA) is a simple yet effective search technique, which has displayed good performance in tackling some optimization problems. However, as the complexity of the problem to be solved increases, AEFA has drawbacks such as premature convergence and insufficient solution accuracy. To mitigate these limitations, a novel variant of AEFA with dynamic neighborhood learning (DNLAEFA) is proposed. In DNLAEFA, a distinctive neighborhood is built for each individual. Then, a dynamic neighborhood learning strategy is adopted to facilitate information exchange between the current individual and its neighbors. Further, a combined parabolic-linear reduction scheme for the electrostatic force is designed based on the observation that calculating the total force consumes a lot of computing resources. Besides, a time-varying attenuation coefficient is employed to regulate the decreasing rate of the Coulomb's constant, which better balances exploration and exploitation. The performance of DNLAEFA is investigated using 43 benchmark functions with diverse properties and three chaotic time series prediction cases. Simulation results indicate that DNLAEFA is more competitive than six existing AEFAs and 27 well-regarded algorithms.

关键词： artificial electric field algorithm dynamic neighborhood learning attenuation coefficient chaotic time series

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EAEFA-R: Multiple learning-based ensemble artificial electric field algorithm for global optimization

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KNOWLEDGE-BASED SYSTEMS 2025年 318卷

作者： Chauhan, Dikshit Yadav, Anupam Mallipeddi, Rammohan Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math & Comp Jalandhar 144008 Punjab India Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

Adjusting the search behaviors of swarm-based algorithms is crucial for solving real-world optimization challenges. Researchers have developed ensemble strategies and self-adaptive mechanisms to enhance the optimization ability of individual algorithms by balancing global and local search capabilities. Inspired by these advancements, this paper proposes a physics-based artificial electric field algorithm with three improvement strategies and an attraction-repulsion operator (EAEFA-R) to enhance diversity and escape local optima. These strategies are probabilistically selected using a dynamic adaptation mechanism. The effectiveness of EAEFA-R is assessed through extensive analysis of exploration-exploitation dynamics and diversity, and it is evaluated on two real-parameter test suites, CEC 2017 and CEC 2022, across 10, 20, 30, 50, and 100-dimensional search spaces. Compared to fifteen state-of-the-art algorithms, including AEFA variants and other optimization algorithms, EAEFA-R demonstrates superior solution accuracy, convergence rate, search capability, and stability performance. The overall ranking highlights its exceptional potential for solving challenging optimization problems, outperforming other state-of-the-art algorithms across various dimensions. The MATLAB source code of EAEFA-R is available at https://***/ChauhanDikshit.

关键词： Meta-heuristic algorithms Optimization Ensembling Repulsion strategy artificial electric field algorithm

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A Multi-Strategy artificial electric field algorithm for Numerical Optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2025年

作者： Feng, Zhichao Cheng, Jiatang Guilin Inst Informat Technol Coll Elect Engn Guilin 541100 Guangxi Peoples R China Guilin Univ Technol Coll Mech & Control Engn Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin 541006 Guangxi Peoples R China

artificial electric field algorithm (AEFA) is a metaheuristic optimization algorithm proposed in recent years, which has been successfully applied to address various optimization problems. However, it is likely to converge prematurely or fall into local optima when solving complex problems. To overcome these disadvantages, a multi-strategy artificial electric field algorithm (MAEFA) is proposed in this paper. For the MAEFA algorithm, the global optimal solution information is utilized to improve the diversity of population and global search ability. Then, the adaptive Coulomb's constant is configured to balance the global exploration and local search. Also, a restart strategy is designed to further alleviate the premature convergence. To validate the effectiveness of MAEFA, it is compared with three AEFA algorithms and several other evolutionary algorithms on 14 test problems presented in CEC 2005 and 13 basic benchmark functions. Furthermore, a wind power prediction model based on MAEFA algorithm and back-propagation (BP) neural network is established to investigate its application ability. Experiments show that MAEFA is significantly superior to other algorithms in tackling these benchmark functions with different dimensions. Furthermore, in terms of wind power prediction, the BP neural network model optimized by MAEFA algorithm also provides higher prediction accuracy.

关键词： artificial electric field algorithm multi-strategy optimization wind power prediction

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An oppositional learning and chaotic local search-based artificial electric field algorithm for engineering optimization

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EVOLUTIONARY INTELLIGENCE 2025年第2期18卷 1-25页

作者： Anita, Shrishti Chamoli, Shrishti Yadav, Anupam Sri Dev Suman Uttarakhand Univ Govt Post Grad Coll Gopeshwar Dept Math Chamoli 246401 Uttarakhand India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math & Comp Jalandhar 144008 Punjab India

Metaheuristic algorithms are stochastic optimization techniques inspired by natural phenomena, with their performance driven by two key operators: exploration and exploitation. Despite their success, a common limitation is their susceptibility to premature convergence and slow progress toward optimal solutions. To address these challenges, this study presents an innovative extension of the artificial electric field algorithm (AEFA), a method rooted in charged system search principles. The proposed algorithm incorporates oppositional-based learning (OBL) and an elite chaotic local search mechanism to enhance both global and local search capabilities. OBL improves the exploration of the global search space, while the elite chaotic local search focuses on refining solutions around near-optimal regions, ensuring accelerated convergence. This hybrid approach is tested on CEC2020 bound-constrained numerical optimization problems and real-world applications such as topology optimization and gear train design. The findings demonstrate the superiority of the proposed algorithm in terms of achieving better optimal solutions, faster convergence rates, and reduced algorithmic complexity when compared to existing state-of-the-art algorithms. Some notable performance of the proposed algorithm are, it demonstrates superior performance over all listed algorithms for 10D problems f1,f2,f3,f5,f7,f9,f10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_1, f_2, f_3, f_5, f_7, f_9, f_{10}$$\end{document}, and most 20D problems f1,f2,f3,f5,f7,f8,f9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_1, f_2, f_3, f_5, f_7, f_8, f_9$$\e

关键词： artificial electric field algorithm Oppositional learning Chaotic local search Optimization Soft computing

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An artificial electric field algorithm with a variable spiral search and an optimal solution mutation strategy

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2025年第1期16卷

作者： Cheng, Jiatang Feng, Zhichao 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

artificial electric field algorithm (AEFA) inspired by Coulomb's law of electrostatic force is a swarm intelligent optimization algorithm. It utilizes the electrostatic force to enable information transmission and evolution of individuals. The traditional AEFA has achieved some successful applications, but it still has problems with premature convergence and low search ability. To solve these problems, an AEFA with a variable spiral search strategy and an optimal solution mutation strategy (VOAEFA) is proposed. The variable spiral search strategy which includes spiral search and skip search can improve the global exploration capability and reduce the probability of the algorithm falling into local optima. The optimal solution mutation strategy can increase the diversity of population. To evaluate the performance of VOAEFA, it is compared with AEFA algorithms and other algorithms on 57 benchmark functions and three engineering design problems. The comparison results on benchmark functions indicate that VOAEFA is superior to its competitors. In addition, VOAEFA has the advantage of high accuracy in solving challenging problems.

关键词： artificial electric field algorithm optimization benchmark functions engineering design problems

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artificial electric field algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-24页

作者： Bi, Jian Zhou, Guo Zhou, Yongquan Luo, Qifang Deng, Wu Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing 100088 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Hebei Peoples R China

The multiple traveling salesman problem (MTSP) is an extension of the traveling salesman problem (TSP). It is found that the MTSP problem on a three-dimensional sphere has more research value. In a spherical space, each city is located on the surface of the Earth. To solve this problem, an integer-serialized coding and decoding scheme was adopted, and artificial electric field algorithm (AEFA) was mixed with greedy strategy and state transition strategy, and an artificial electric field algorithm based on greedy state transition strategy (GSTAEFA) was proposed. Greedy state transition strategy provides state transition interference for AEFA, increases the diversity of population, and effectively improves the accuracy of the algorithm. Finally, we test the performance of GSTAEFA by optimizing examples with different numbers of cities. Experimental results show that GSTAEFA has better performance in solving SMTSP problems than other swarm intelligence algorithms.

关键词： artificial electric field algorithm State transfer strategy Spherical geometry Spherical multiple traveling salesman problem Metaheuristic optimization

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artificial electric field algorithm to extract nine parameters of triple-diode photovoltaic model

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第1期45卷 590-604页

作者： Selem, Sameh, I El-Fergany, Attia A. Hasanien, Hany M. Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig 44519 Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo Egypt

This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.

关键词： artificial electric field algorithm dynamic real simulations optimization algorithms photovoltaic modeling renewable energy

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artificial electric field algorithm with inertia and repulsion for spherical minimum spanning tree

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APPLIED INTELLIGENCE 2022年第1期52卷 195-214页

作者： Bi, Jian Zhou, Yongquan Tang, Zhonghua Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

artificial electric field algorithm (AEFA) is a potential global optimization algorithm proposed in recent years and has been successfully applied to various engineering optimizations. However, precocious convergence tends to occur when solving complex engineering optimization problems. To avoid premature convergence to some extent, an artificial electric field algorithm with inertia and repulsion (IRAEFA) is proposed. The IRAEFA algorithm introduces the inertia mechanism and the repulsion between charges, expands the search space, increases the diversity of population, balances the exploration and development ability of the algorithm, and avoids the algorithm falling into the local optimal solution. Finally, the IRAEFA algorithm is used to solve the spherical mining spanning tree (MST) problem, and the results obtained are compared and analyzed with the results of other well-known metaheuristics optimization algorithms. Experimental results show that the proposed algorithm has better performance than other algorithms in solving spherical MST problems.

关键词： artificial electric field algorithm Spherical MST Prü fer Spherical geometry Physics-based optimization Metaheuristic

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Stability and agent dynamics of artificial electric field algorithm

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JOURNAL OF SUPERCOMPUTING 2024年第1期80卷 835-864页

作者： Chauhan, Dikshit Yadav, Anupam Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144008 Punjab India

The artificial electric field algorithm (AEFA) is a recently developed optimization algorithm inspired by the principles of electrostatic force and the law of motion. It operates as a stochastic population-based algorithm and utilizes probabilistic techniques to search for solutions. Due to its stochastic nature, it is crucial to investigate the numerical stability of AEFA. Such a study helps in determining optimal parameter values for the AEFA scheme. In this article, we analyse the stability of AEFA using two different criteria: Von Neumann stability and agent dynamics. Both methods provide sufficient conditions for the stability of AEFA, enabling the adaptation of its parameters. We further examine the trajectory behaviour of agents by evaluating various benchmark functions from the CEC 2021 test suite. Through illustrative examples, we demonstrate the theoretical and experimental studies, highlighting the stable nature of AEFA and validating the proposed stability conditions.

关键词： Passive system Convergence artificial electric field algorithm Lyapunov stability Von Neumann stability

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An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第14期45卷 20199-20218页

作者： Houssein, Essam H. Hashim, Fatma A. Ferahtia, Seydali Rezk, Hegazy Minia Univ Fac Comp & Informat Al Minya Egypt Helwan Univ Fac Engn Cairo Egypt Univ Msila Dept Elect Engn Lab Anal Signaux & Syst Msila Algeria Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Minia Univ Fac Engn Al Minya Egypt

The artificial electric field algorithm (AEFA) is a recent physics population-based optimization approach inspired by Coulomb's law of electrostatic force and Newton's law of motion. In this paper, an alternative version of AEFA called mAEFA is proposed to boost the searchability and the balance between the explorations to the exploitation of the original AEFA. To escape dropping on the local points in the mAEFA, three efficient strategies for instance;modified local escaping operator (MLEO), levy flight (LF), and opposition-based learning (OBL), are in conjunction with the original AEFA. The convergence rate will be improved when the best agent is identified;thus, stagnation at a local solution can be efficiently avoided. To assess the performance of the proposed mAEFA, it has been evaluated over the CEC'2020 test functions. Furthermore, a robust methodology based on mAEEA is proposed to identify the best parameters of PEM fuel cell (PEMFC). The model of the PEMFC includes nonlinear characteristics that involve several unknown design variables. Thus, it is challenging to develop an accurate model. There are seven design variables to be tuned to reach the targeted dependable model. Two different types of PEMFCs: NedStack PS6 and SR-12 500 W were used to demonstrate the superiority of the mAEEA. Throughout the optimization process, the unidentified parameters of PEMFC are appointed to be decision variables. But the objective function, which necessary to be least is represented by the SSE between the calculated PEMFC voltage and the experimental one. Nine recent optimizers are used in the comparison with the proposed mAEEA. According to the main findings, the advantage of the proposed mAEEA in determining the best PEMFC parameters is verified compared to the other optimizers. Lowest SSE, lowest RMSE, minimum stranded deviation, maximum efficiency, and high coefficient of determination are achieved by the proposed mAEEA.

关键词： artificial electric field algorithm CEC'2020 test functions fuel cell optimization problems parameter estimation

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