Journal
MEMETIC COMPUTING
Volume 8, Issue 1, Pages 45-61Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12293-015-0175-9
Keywords
Big optimization problems; Decomposition; Evolutionary multi-objective optimization; Gradient methods; Memetic algorithms
Funding
- Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) [61522311]
- General Program of NSFC [61271301]
- Overseas, Hong Kong & Macao Scholars Collaborated Research Program of NSFC [61528205]
- Research Fund for the Doctoral Program of Higher Education of China [20130203110010]
- Fundamental Research Funds for the Central Universities [K5051202052]
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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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