Abstract
AbstractArtificial bee colony (ABC) algorithm shows good performance on many optimization problems. However, most ABC variants focus on single objective optimization problems. In this paper, an improved bare-bones multi-objective artificial bee colony (called BMOABC) algorithm is proposed to solve multi-objective optimization problems (MOPs). Fast non-dominated sorting is used to select non-dominated solutions. The crowded-comparison operator is employed to maintain population diversity. To enhance the search ability, an improved bare-bones strategy is utilized. The fitness function is modified to handle multiple objective values. Then, a novel probability selection model is designed for the onlooker bees. To verify the effectiveness of BMOABC, five benchmark MOPs are employed in the experiment. Experimental results show that BMOABC is superior to three other multi-objective algorithms.KeywordsArtificial bee colonyMulti-objective optimizationMulti-objective artificial bee colonySelection probability
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