Information Exchange Particle Swarm Optimization for Multitasking
CHENG Meiying1, QIAN Qian2, NI Zhiwei3, ZHU Xuhui3
1.Business School, Huzhou University, Huzhou 313000; 2.School of Teacher Education, Huzhou University, Huzhou 313000; 3.Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, School of Management, Hefei University of Technology, Hefei 230009
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.
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2019, Vol. 32 
