Dynamic multi-objective differential evolution for solving constrained optimization problem

Dynamic constrained multi-objective differential evolution (DCMODE) is designed for solving constrained optimization problem (COP). Main feature presented in this paper is to construct dynamic multi-objective optimization problem (DMOP) from COP. The two evolved objectives are original function obje...

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Veröffentlicht in:2011 IEEE Congress of Evolutionary Computation (CEC) S. 2649 - 2654
Hauptverfasser: Lina Jia, Sanyou Zeng, Dong Zhou, Aimin Zhou, Zhengjun Li, Hongyong Jing
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2011
Schlagworte:
Benchmark testing
Constrained optimization
Differential Evolution
Dynamic Constrained Multi-objective Optimization
Electronic mail
Evolutionary computation
Geology
Heuristic algorithms
Optimization
Search problems
ISBN:1424478340, 9781424478347
ISSN:1089-778X
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Zusammenfassung:Dynamic constrained multi-objective differential evolution (DCMODE) is designed for solving constrained optimization problem (COP). Main feature presented in this paper is to construct dynamic multi-objective optimization problem (DMOP) from COP. The two evolved objectives are original function objective and violation objective. Constraints are controlled by dynamic environments, where the relaxed constraints boundaries are gradually tightened to original boundaries. After this dynamic process, DMOP solutions are close to COP solution. This new algorithm is tested on benchmark problems of special session at CEC2006 with 100% success rates of all problems. Compared with several state-of-the-art DE variants referred in this paper, our algorithm outperforms or performs similarly to them. The satisfactory results suggest that it is efficient and generic when handling inequality/equality constraints.
ISBN:1424478340
9781424478347
ISSN:1089-778X
DOI:10.1109/CEC.2011.5949949