An External Archive-Guided Multiobjective Particle Swarm

Optimization Algorithm

IEEE Transactions on Cybernetics (TCYB)

Qingling Zhu¹    Qiuzhen Lin²    Weineng Chen³    Ka-Chun Wong⁴    Carlos A. Coello Coello⁵    Jianqiang Li²    Jianyong Chen²    Jun Zhang³

¹City University of Hong Kong    ²Shenzhen University    ³South China University of Technology   

⁴City University of Hong Kong    ⁵CINVESTAV-IPN (Evolutionary Computation Group)

Abstract

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Fig. 1 The algorithmic framework of AgMOPSO

(a)                                                 (b)

Fig. 2 The procedure of immune-based evolutionary search

Fig. 3 Two variants of AgMOPSO

Fig. 4 The running times of all the compared algorithms on WFG test problems

Fig. 5 Final results of C-AgMOPSO on the car-side impact problem

Acknowledgements

Thiswork was supported in part by the National Natural Science Foundation of China under Grant 61402291, Grant 61672358, and Grant 61622206, and in part by CONACyT under Grant 221551.

Bibtex

@ARTICLE{7946155,

author={Zhu, Qingling and Lin, Qiuzhen and Chen, Weineng and Wong, Ka-Chun and Coello Coello, Carlos A. and Li, Jianqiang and Chen, Jianyong and Zhang, Jun},

journal={IEEE Transactions on Cybernetics},

title={An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm},

year={2017},

}

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