Constrained optimization problem solved by dynamic constrained NSGA-III multiobjective optimizational techniques

This paper proposes dynamic constrained version of NSGA-III to handle constraints for constrained optimization problems (COPs). The methodology first constructs a dynamic constrained multi-objective optimization problem (DCMOP) equivalent to the COP by converting the constraints into some violation...

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Veröffentlicht in:IEEE transactions on evolutionary computation S. 2923 - 2928
Hauptverfasser: Xi Li, Sanyou Zeng, Sha Qin, Kunqi Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2015
Schlagworte:
Benchmark testing
Constrained optimization
dynamic constrained handling technology
Integrated circuits
Multi-objective optimization
NSGA-III
ISSN:1089-778X
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Zusammenfassung:This paper proposes dynamic constrained version of NSGA-III to handle constraints for constrained optimization problems (COPs). The methodology first constructs a dynamic constrained multi-objective optimization problem (DCMOP) equivalent to the COP by converting the constraints into some violation objective functions and gradually shrinking the initially broadened boundary to the original one. Then a dynamic constrained version of the state-of-the-art NSGA-III is implemented to solve the DCMOP. Differential evolution (DE) is used as the evolutionary algorithm to generate offspring. Experimental results show that it is competitive to peer algorithm referred in this paper, and has better performance on global search.
ISSN:1089-778X
DOI:10.1109/CEC.2015.7257252