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Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

Intelligent Automation & Soft Computing 2023年第9期37卷 2783-2807页

作者： Dongping Tian Bingchun Li Jing Liu Chen Liu Ling Yuan Zhongzhi Shi School of Computer Science and Technology Kashi UniversityKashi844006China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence ***,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal *** this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the ***,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution *** be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution *** by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm *** addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO ***,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

关键词： Particle swarm optimization local optima acceleration coefficients swarm diversity premature convergence

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Improved Optimization Algorithm Based on Random Mutation Strategy

Improved Optimization Algorithm Based on Random Mutation Str...

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第30届中国控制与决策会议

作者： Xiuhong Zhao Hongyu Wang Physics Department Anshan Normal University

Harmony Search（HS） algorithm is a good meta-heuristic intelligent optimization method,which has been paid much attention recently by many ***,there is a shortage of these intelligent optimization methods are easily trapped into local *** is no exception.A new variant of harmony search algorithm with random mutation strategy（HSRM）,which is used to improve the performance of HS is proposed in this *** HSRM uses a random mutation strategy to instead of the pitch adjusting operation,and dynamically adjust the key parameters pitch adjusting rate（PAR）.Furthermore,experiment results demonstrated that the proposed method is superior to the HS and recently developed variants（IHS,and GHS） and other meta-heuristic algorithms.

关键词： Harmony search algorithm local optima random mutation strategy

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