Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 22, Issue 4, Pages 609-622Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2017.2749619
Keywords
Adaptive reference point; evolutionary multiobjective optimization; indicator-based selection; many-objective optimization
Funding
- National Natural Science Foundation of China [61672033, 61502004, 61502001]
- Joint Research Fund for Overseas Chinese, Hong Kong, and Macao Scholars of the National Natural Science Foundation of China [61428302]
- U.K. Engineering and Physical Sciences Research Council [EP/M017869/1]
- EPSRC [EP/M017869/1] Funding Source: UKRI
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During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.
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