Adaptive multifactorial particle swarm optimisation

• 作者: Maoguo Gong Zedong Tang
• 来源期刊: 智能技术学报
• 学科: 社会科学
• 关键词: MFPSO MULTIFACTORIAL PARTICLE SWARM optimisation
• 年，卷（期）: 2019,（1）
• 页码范围: 37-46
• 页数: 10页
• 分类号: G

摘要

Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task particles.Meanwhile,these algorithms use a fixed inter-task learning probability throughout the evolution process.However,the parameter is problem dependent and can be various at different stages of the evolution.In this work,the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in MFPSO.This inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task crossover.By this mean,the particles can explore a broad search space when utilising the additional searching experiences of other tasks.In addition,to enhance the performance on problems with different complementarity,they design a self-adaption strategy to adjust the inter-task learning probability according to the performance feedback.They compared the proposed algorithm with the state-of-the-art algorithms on various benchmark problems.Experimental results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.