A MODIFIED PARTICLE SWARM OPTIMIZATION WITH MUTATION AND REPOSITION

Chiabwoot Ratanavilisagul; Boontee Kruatrachue

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A MODIFIED PARTICLE SWARM OPTIMIZATION WITH MUTATION AND REPOSITION

机译：带有突变和位置的修正粒子群优化

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The common problems of particle swarm optimization (PSO) are those of trapping in local optimum and premature convergence. This research paper aims to develop a solution to both problems by introducing mutation around particles and employing the reposition technique. The concurrent use of the introduced mutation and reposition has proved to solve both problems and enhanced the PSO performance; and thus is employed in this research. The proposed technique is termed MRPSO. MRPSO is tested on sixteen benchmark functions and the multidimensional knapsack problems (MKP). MRPSO yields the more satisfactory search results than the genetic algorithm (GA) and PSOs for the benchmark functions and the MKPs.

机译：粒子群优化（PSO）的常见问题是陷入局部最优和过早收敛的问题。本研究论文旨在通过引入粒子周围的突变并采用重新定位技术来开发针对这两个问题的解决方案。事实证明，同时使用引入的突变和重新定位可以解决这两个问题并提高PSO性能。因此被用于这项研究。提出的技术称为MRPSO。 MRPSO已针对16种基准功能和多维背包问题（MKP）进行了测试。对于基准功能和MKP，与遗传算法（GA）和PSO相比，MRPSO产生的搜索结果更为令人满意。

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来源
《International Journal of Innovative Computing Information and Control》 |2014年第6期|2127-2142|共16页
作者
Chiabwoot Ratanavilisagul; Boontee Kruatrachue;
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作者单位

Department of Computer Engineering Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang Chalongkrung Rd., Ladkrabang, Bangkok 10520, Thailand;

Department of Computer Engineering Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang Chalongkrung Rd., Ladkrabang, Bangkok 10520, Thailand;

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正文语种 eng
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关键词
Particle swarm optimization; Binary particle swarm optimization; Mutation operator; Multidimensional knapsack problem; Genetic algorithm;

机译：粒子群优化;二元粒子群优化;变异运算子;多维背包问题;遗传算法;

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1. Dynamic Population Size and Mutation Round Strategy Assisted Modified Particle Swarm Optimization with Mutation and Reposition [J] . Chiabwoot Ratanavilisagul Procedia Computer Science . 2016,第1期

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2. An integrated particle swarm optimization approach hybridizing a new self-adaptive particle swarm optimization with a modified differential evolution [J] . Neural computing & applications . 2020,第9期

机译：综合粒子群优化方法用改进的差分演进杂交新的自适应粒子群优化
3. Multi-Swarm Particle Swarm Optimizer with Mutation and Its Research in Biomedical Information Classification Optimizer [J] . Li Mi, Chen Huan, Zhang Ming, Journal of Medical Imaging and Health Informatics . 2018,第8期

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4. Analysis of Gaussian Cauchy Mutations in Modified Particle Swarm Optimization Algorithm [C] . Archana Sarangi, Sonali Samal, Shubhendu Kumar Sarangi International Conference on Advanced Computing and Communication Systems . 2019

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6. Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization [O] . Khubab Ahmed, Peng Yan, Su Li 2021

机译：使用改进的粒子群优化的压电式纳米级基于模型的基于模型的基于模型的滞后识别
7. Dynamic Population Size and Mutation Round Strategy Assisted Modified Particle Swarm Optimization with Mutation and Reposition [O] . Ratanavilisagul Chiabwoot 2016

机译：带有变异和重新定位的动态种群规模和变异回合策略辅助的修正粒子群优化

A MODIFIED PARTICLE SWARM OPTIMIZATION WITH MUTATION AND REPOSITION

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