信息交互多任务粒子群算法

doi:10.16451/j.cnki.issn1003-6059.201905001

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Abstract Different from the existing cloud platform and parallel computer, the implicit parallelism of particle swarm optimization(PSO) is fully exploited in this paper. Two information exchange strategies, within-task information transfer and between-task information transfer, are involved. Moreover, factorial rank, scalar fitness and skill factor are introduced into PSO for multitasking. In each iteration, the most appropriate individuals are used to solve the most suitable task, and information exchange PSO for multitasking(IEPSOM) is proposed. Multitasking function optimization problems, multitasking multiple constraints Engineering design cases and multitasking key evaluation system constructing problems are involved to verify the performance of IEPSOM. Experimental results reveal that in IEPSOM multitasking environment, information transfer enhances the solutions quality and speeds up the convergence.

Key words： Multitasking Particle Swarm Optimization(PSO) Information Exchange Multitasking Function Optimization Multitasking Multiple Constraints Engineering Design Multitasking Key Evaluation System Constructing

Received: 08 October 2018

ZTFLH:

TP 18

Fund:Supported by Training Program of Major Research Plan of National Natural Science Foundation of China(No.91546108), National Natural Science Foundation of China(No.71701061), Annual Research Topics of Educational Science Planning of Zhejiang Province(No.2019SCG036), Science and Technology planning project of Huzhou(No.2018YZ11), Natural Science foundation of Huzhou University(No.2018XJKJ51,2018XJKJ56)

Corresponding Authors: (CHENG yinmei(Corresponding author), Ph.D., lecturer. Her research interests include swarm intelligent algorithm and data mining.)

About author:: (QIAN Qian, master, lecturer. His research interests include evolutionary computation.)(NI Zhiwei, Ph.D., professor. His research interests include data mining, machine lear-ning and artificial intelligence.)(ZHU Xuhui, Ph.D., lecturer. His research interests include intelligent computation and machine learning.)

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	CHENG Meiying¹
	QIAN Qian²
	NI Zhiwei³
	ZHU Xuhui³

Cite this article:

CHENG Meiying¹,QIAN Qian²,NI Zhiwei³等. Information Exchange Particle Swarm Optimization for Multitasking[J]. , 2019, 32(5): 385-397.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201905001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I5/385

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