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In multi-objective optimization, the quality of Pareto-optimal solutions is evaluated by the efficiency of the optimal front (proximity), uniformity, and spread. This paper introduces a novel search scheme for multi-objective evolutionary algorithms (MOEAs), whose solutions demonstrate improved proximity and spread metrics. Our proposed scheme comprises two search phases with different search objectives...
In multiobjective optimization problems, it is important to derive solutions with high accuracy, uniform distribution, and broadness. Here, we propose a two-phase search process to improve the accuracy and broadness of solutions. In the first phase, the accuracy of the solutions is improved, and a reference point specified by a decision maker is set and utilized for the search. In the second phase,...
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