Abstract
This paper defines a new multi-objective optimization problem, called multiple multi-objective optimization problem (MMOP). An MMOP is composed of several multi-objective optimization problems (MOPs) with different decision spaces and the same objective space, and its optimal solutions are non-dominated solutions among Pareto optimal solutions of all the individual MOPs. We construct a set of benchmark test instances with different characteristics. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.
This work was supported by the National Key Research and Development Project, Ministry of Science and Technology, China (Grant No. 2018AAA0101301) and the National Natural Science Foundation of China (Grant No. 61876163).
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Chen, J., Zhang, Q., Li, G. (2021). MOEA/D for Multiple Multi-objective Optimization. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_13
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DOI: https://doi.org/10.1007/978-3-030-72062-9_13
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