Co-evolutionary Particle Swarm Optimization for Multitasking
CHENG Meiying1, QIAN Qian2, NI Zhiwei3, ZHU Xuhui3,4
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
4.Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens 45701
The traditional particle swarm optimization(PSO) and its improved version aim to tackle the single task. With the development of electronic business, online severs need to deal with a batch of requests simultaneously, i.e. multitasking. Different from the parallel computer, the implicit parallelism of PSO is fully exploited, and co-evolution theory is introduced for multitasking. In the multitasking environment, different tasks correspond to different subpopulations, and the useful information is transferred from one subpopulation to another with a certain probability. Thus, co-evolutionary PSO for multitasking(CPSOM) is proposed in this paper. To verify the effectiveness of the proposed algorithm, CPSOM is used to solve a batch of function test problems, feature selection problems and constrained engineering optimization problems. Experimental results show that the useful information can be autonomously transferred from one task to another in the CPSOM environment. Moreover, cooperation of different tasks enhance the solution quality and speed up the convergence.
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2018, Vol. 31 
