Artificial bee colony with enhanced food locations for solving mechanical engineering design problems

Abstract

Artificial Bee colony (ABC) simulates the intelligent foraging behavior of bees. ABC consists of three kinds of bees: employed, onlooker and scout. Employed bees perform exploration and onlooker bees perform exploitation whereas scout bees are responsible for randomly searching the food source in the feasible region. Being simple and having fewer control parameters ABC has been widely used to solve complex multifaceted optimization problems. ABC performs well at exploration than exploitation. The success of any nontraditional algorithm depends on these two antagonist factors. Focusing on this limitation of ABC, in this study the food locations in basic ABC are enhanced using Opposition based learning (OBL) concept. This variant is improved by incorporating greediness in searching behavior and named as I-ABC greedy. The modifications help in maintaining population diversity as well as enhance exploitation. The proposal is validated on seven mechanical engineering design problems. The simulated results have been noticed competent with that of state-of-art algorithms.