Constrained Multi-Objective GA Optimization Using Reduced Models
نویسندگان
چکیده
In this paper we propose a novel approach for solving constrained multi-objective optimization problems using a steady state GA and reduced models. Our method called Objective Exchange Genetic Algorithm for Design optimization (OEGADO) is intended for solving real-world application problems that have many constraints and very small feasible regions. OEGADO runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with others. Empirical results in benchmark and engineering design domains are presented. A comparison between OEGADO and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows that OEGADO performed better than NSGA-II for difficult problems, and found Pareto-optimal solutions in fewer objective evaluations. The results suggest that our method may be better for solving real-world application problems wherein the objective computation time is large.
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