Multiobjective evolutionary algorithms based on target region preferences

Longmei Li

doi:10.1016/j.swevo.2018.02.006

Multiobjective evolutionary algorithms based on target region preferences

2018-06-01

31

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AI

DOI：10.1016/j.swevo.2018.02.006

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Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference. based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.

Keyword:

Target region

Preferences

Multiobjective evolutionary algorithms (MOEA)

Satellite mission planning

Multiobjective evolutionary algorithms based on target region preferences

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