Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 23, Issue 2, Pages 247-257Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2848254
Keywords
Decomposition; evolutionary algorithm; irregular Pareto front (PF); many-objective optimization; reference vector; sorting
Funding
- National Natural Science Foundation of China [61773029, 61273230, 61603010, 61603011]
- China Scholarship Council, Research Base of Beijing Modern Manufacturing Industry Development
- Beijing Social Science Foundation of China [16JDGLC005]
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Evolutionary algorithms have shown their promise in coping with many-objective optimization problems. However, the strategies of balancing convergence and diversity and the effectiveness of handling problems with irregular Pareto fronts (PFs) are still far from perfect. To address these issues, this paper proposes an adaptive sorting-based evolutionary algorithm based on the idea of decomposition. First, we propose an adaptive sorting-based environmental selection strategy. Solutions in each subpopulation (partitioned by reference vectors) are sorted based on their convergence. Those with better convergence are further sorted based on their diversity, then being selected according to their sorting levels. Second, we provide an adaptive promising subpopulation sorting-based environmental selection strategy for problems which may have irregular PFs. This strategy provides additional sorting-based selection effort on promising subpopulations after the general environmental selection process. Third, we extend the algorithm to handle constraints. Finally, we conduct an extensive experimental study on the proposed algorithm by comparing with start-of-the-state algorithms. Results demonstrate the superiority of the proposed algorithm.
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