文章基本信息

标题：Population based optimization algorithms improvement using the predictive particles
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作者：M. M. H. Elroby ; S. F. Mekhamer ; H. E. A. Talaat 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2020
卷号：10
期号：3
页码：3261-3274
DOI：10.11591/ijece.v10i3.pp3261-3274
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate.
关键词：Particle swarm optimization;Population optimization;Predictive particle;Teaching learning based optimization