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

Artificial Bee Colony (ABC) is one of the recently introduced optimization methods based on intelligent behavior of honey bees. In this work, we propose an Adaptive Multi Objective Artificial Bee Colony (A-MOABC) Optimizer which uses Pareto dominance notion and takes advantage of crowding distance and windowing mechanisms. The employed bees use an adaptive windowing mechanism to select their own leaders and alter their positions. Besides, onlookers update their positions using food sources presented by employed bees. Pareto dominance notion is used to show the quality of the food sources. Those employed or onlooker bees which find food sources with poor quality turn into scout bees in order to search other areas. The suggested method uses crowding distance technique in conjunction with the windowing mechanism in order to keep diversity in the external archive. The experimental results indicate that the proposed approach is not only thoroughly competitive compared to other algorithms considered in this work, but also finds the result with satisfactory precision.