A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

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 regi...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 24; H. 4; S. 792 - 806
Hauptverfasser: Chen, Qingda, Ding, Jinliang, Yang, Shengxiang, Chai, Tianyou
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Change detection
Change response
Concavity
Constraints
Convergence
Convexity
Design optimization
dynamic constrained multiobjective optimization
Evolutionary algorithms
Heuristic algorithms
Linear programming
Multiple objective analysis
Optical fibers
Optimization
Pareto optimization
Population
population selection
Sociology
Statistics
Strategy
test problems
ISSN:1089-778X, 1941-0026
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Zusammenfassung: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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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2019.2958075