Title :
An Evolutionary Algorithm for Constrained Multi-objective Optimization Problems
Author :
Min, Hua-Qing ; Zhou, Yu-Ren ; Lu, Yan-sheng ; Jiang, Jia-zhi
Author_Institution :
Coll. of Comput. Sci. & Eng., HuaZhong Univ. of Sci. & Technol., Wuhan
Abstract :
Constrained multi-objective optimization problems (CMOP) are challenging and difficult to solve. In this paper, a simple and practical evolutionary algorithm for constrained multi-objective optimization problems (EACMOP) is presented, by defining constraints using non-parameter punitive functions, using Pareto strength value to represent Pareto order strength among individuals and using crowding density to ensure group diversity. It defines the evolutionary algorithm fitness functions by combining constraint treatment, comparison of Pareto strength optimization and crowding density. Test results on several benchmark functions showed that the approach is effective and robust
Keywords :
Pareto optimisation; evolutionary computation; Pareto order strength; Pareto strength optimization; Pareto strength value; constrained multiobjective optimization problem; constraint treatment; crowding density; evolutionary algorithm; group diversity; nonparameter punitive functions; Algorithm design and analysis; Benchmark testing; Computer science; Constraint optimization; Design optimization; Diversity reception; Educational institutions; Evolutionary computation; Pareto optimization; Robustness; Evolutionary algorithms; constrained; multi-objective optimization;
Conference_Titel :
Services Computing, 2006. APSCC '06. IEEE Asia-Pacific Conference on
Conference_Location :
Guangzhou, Guangdong
Print_ISBN :
0-7695-2751-5
DOI :
10.1109/APSCC.2006.30
