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.