期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 24, 期 4, 页码 792-806出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2019.2958075
关键词
Optical fibers; Sociology; Statistics; Heuristic algorithms; Linear programming; Optimization; Convergence; Change response; dynamic constrained multiobjective optimization; population selection; test problems
资金
- National Natural Science Foundation of China [61988101, 61525302, 61590922, 61673331]
- National Key Research and Development Program of China [2018YFB1701104]
- Xingliao Plan of Liaoning Province [XLYC1808001]
To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.
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