Adaptive multifactorial particle swarm optimisation

作     者：Zedong Tang Maoguo Gong 

作者机构：School of Electronic EngineeringKey Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityNo.2 South TaiBai RoadXi’an 710071People’s Republic of China 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2019年第4卷第1期

页      面：37-46页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 0839[工学-网络空间安全] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China: 61772393 

主　　题：MFPSO multifactorial particle swarm optimisation 

摘      要：Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task ***,these algorithms use a fixed inter-task learning probability throughout the evolution ***,the parameter is problem dependent and can be various at different stages of the *** this work,the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in *** inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task *** this mean,the particles can explore a broad search space when utilising the additional searching experiences of other *** 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 *** compared the proposed algorithm with the state-of-the-art algorithms on various benchmark *** results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.