Full Elite Sets for Multi-objective Optimisation
Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of frontal estimates found using the full archive is illustrated on test problems.
KeywordsPareto Front Multiobjective Optimization Multiobjective Evolutionary Algorithm Strength Pareto Evolutionary Algorithm True Pareto Front
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