A New Evolutionary Algorithm for Multi-objective Optimization Problems
نویسنده
چکیده
Among the currently successful Evolutionary Multi-Objective Algorithms (MOEAs), elitism and no sharing factor are two common characteristics and have been demonstrated to improve performance significantly. Based on these two principles, two heuristics, with which impressive improvements were showed in single objective optimization, are introduced in a newly designed EMOA in this paper: multi-parent crossover, which ensures that the population converges to the true Pareto optimal front; and swarm hill climbing, which effectively helps prevent premature convergence and achieve a well distributed trade-off front.
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تاریخ انتشار 2003