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

• Published in: Journal of ambient intelligence and humanized computing Vol. 11; no. 1; pp. 267 - 290
• Main Authors: Sharma, Tarun K., Abraham, Ajith
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bees; Computational Intelligence; Continuous casting; Design engineering; Engineering; Exploitation; Food; Foraging behavior; Literature reviews; Mechanical engineering; Mechanical engineering design; Methods; Optimization; Original Research; Robotics and Automation; Searching; Swarm intelligence; User Interfaces and Human Computer Interaction; Artificial bee colony; Engineering design problems; Constrained Optimization; Opposition based learning; Convergence
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-019-01265-7

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.