Hybrid Methods for Multi-objective Evolutionary Algorithms
نویسندگان
چکیده
Hybrid methods of using evolutionary algorithms with a local search method are often used in the context of singleobjective real-world optimization. In this paper, we discuss a couple of hybrid methods for multi-objective realworld optimization. In the posteriori approach, the obtained non-dominated solutions of a multi-objective evolutionary algorithm (MOEA) run are modified using a local search method. In the online approach, a local search method is applied to each solution obtained by genetic operations in a MOEA run. Both these approaches are compared on two engineering shape optimization problems for a fixed number of trials. Simulation results suggest important insights about the extent of local search and the extent of an MOEA needed to achieve an overall efficient hybrid approach.
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