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Bee colony optimization part II: The application survey

Bee Colony Optimization (BCO) is a meta–heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. | Yugoslav Journal of Operations Research 25 (2015), Number 2, 185–219 DOI: 10.2298/YJOR131029020T Invited survey BEE COLONY OPTIMIZATION PART II: THE APPLICATION SURVEY ´ Duˇsan TEODOROVIC Faculty of Transport and Traffic Engineering, University of Belgrade dusan@sf.bg.ac.rs ˇ ´ Milica SELMI C Faculty of Transport and Traffic Engineering, University of Belgrade m.selmic@sf.bg.ac.rs ´ Tatjana DAVIDOVIC Mathematical Institute, Serbian Academy of Sciences and Arts tanjad@mi.sanu.ac.rs Received: October 2013 / Accepted: November 2013 Abstract: Bee Colony Optimization (BCO) is a meta–heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications. Keywords: Meta–Heuristic Methods, Swarm Intelligence, Combinatorial Optimization, Routing, Location, Scheduling Problems. MSC: 90-03, 68W20, 90BXX. 1. INTRODUCTION The Bee Colony Optimization (BCO) meta-heuristic, inspired by foraging behavior of honeybees, was proposed in [18, 19, 20, 21] for dealing with the well 186 ˇ D. Teodorovi´c, M. Selmi´ c, T. Davidovi´c / BCO PART II: The Application Survey known hard combinatorial optimization problems: travelling salesman and vehicle routing. The plan was to build the multi agent system (a colony of artificial bees) able to efficiently solve hard optimization problems. BCO is a stochastic, random-search population-based technique. It was motivated by the analogy found between the natural behavior of