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A multi-objective memetic algorithm based on decomposition for big optimization problems

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Abstract

When solving multi-objective optimization problems (MOPs) with big data, traditional multi-objective evolutionary algorithms (MOEAs) meet challenges because they demand high computational costs that cannot satisfy the demands of online data processing involving optimization. The gradient heuristic optimization methods show great potential in solving large scale numerical optimization problems with acceptable computational costs. However, some intrinsic limitations make them unsuitable for searching for the Pareto fronts. It is believed that the combination of these two types of methods can deal with big MOPs with less computational cost. The main contribution of this paper is that a multi-objective memetic algorithm based on decomposition for big optimization problems (MOMA/D-BigOpt) is proposed and a gradient-based local search operator is embedded in MOMA/D-BigOpt. In the experiments, MOMA/D-BigOpt is tested on the multi-objective big optimization problems with thousands of variables. We also combine the local search operator with other widely used MOEAs to verify its effectiveness. The experimental results show that the proposed algorithm outperforms MOEAs without the gradient heuristic local search operator.

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Notes

  1. This is the case of maximization problem. For the minimization problem, minus sub-gradient direction will be used.

  2. http://www.husseinabbass.net/BigOpt.html.

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Acknowledgments

This work is partially supported by the Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) under Grant 61522311, the General Program of NSFC under Grant 61271301, the Overseas, Hong Kong & Macao Scholars Collaborated Research Program of NSFC under Grant 61528205, the Research Fund for the Doctoral Program of Higher Education of China under Grant 20130203110010, and the Fundamental Research Funds for the Central Universities under Grant K5051202052.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Yutong Zhang, Jing Liu, Mingxing Zhou & Zhongzhou Jiang

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  1. Yutong Zhang
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  2. Jing Liu
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  3. Mingxing Zhou
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  4. Zhongzhou Jiang
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Correspondence to Jing Liu.

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Zhang, Y., Liu, J., Zhou, M. et al. A multi-objective memetic algorithm based on decomposition for big optimization problems. Memetic Comp. 8, 45–61 (2016). https://doi.org/10.1007/s12293-015-0175-9

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