AN ADAPTIVE MULTI-OBJECTIVE ARTIFICIAL BEE COLONY WITH CROWDING DISTANCE MECHANISM

Abstract

Artificial Bee Colony (ABC) is one of the recently introduced optimization methodsbased on intelligent behavior of honey bees. In this work, we propose an Adaptive Multi-ObjectiveArtificial Bee Colony (A-MOABC) Optimizer which uses Pareto dominance notion and takesadvantage of crowding distance and windowing mechanisms. The employed bees use an adaptivewindowing mechanism to select their own leaders and alter their positions. Besides, onlookersupdate their positions using food sources presented by employed bees. Pareto dominance notion isused to show the quality of the food sources. Those employed or onlooker bees which find foodsources with poor quality turn into scout bees in order to search other areas. The suggested methoduses crowding distance technique in conjunction with the windowing mechanism in order to keepdiversity in the external archive. The experimental results indicate that the proposed approach isnot only thoroughly competitive compared to other algorithms considered in this work, but alsofinds the result with satisfactory precision.