【摘 要】
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While solving unimodal function problems,conventional meta-heuristic algorithms often suffer from low accuracy and slow convergence.Therefore,in this paper,a novel meta-heuristic optimization algorithm,named protonelectron swarm (PES),is proposed based on
【机 构】
：
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
论文部分内容阅读
While solving unimodal function problems,conventional meta-heuristic algorithms often suffer from low accuracy and slow convergence.Therefore,in this paper,a novel meta-heuristic optimization algorithm,named protonelectron swarm (PES),is proposed based on physical rules.This algorithm simulates the physical phenomena of like-charges repelling each other while opposite charges attracting in protons and electrons,and establishes a mathematical model to realize the optimization process.By balancing the global exploration and local exploitation ability,this algorithm achieves high accuracy and avoids falling into local optimum when solving target problem.In order to evaluate the effectiveness of this algorithm,23 classical benchmark functions were selected for comparative experiments.Experimental results show that,compared with the contrast algorithms,the proposed algorithm cannot only obtain higher accuracy and convergence speed in solving unimodal function problems,but also maintain strong optimization ability in solving multimodal function problems.
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