978-1-5090-2084-3/16/$/31.00©2016 IEEE A Systematic Review of Applications of Bee Colony Optimization Sherry Chalotra Sumeet Kaur Sehra Sukhjit Singh Sehra Research Scholar Assistant Professor, Dept. of CSE Assistant Professor, Dept. of CSE GNDEC, Ludhiana, Punjab GNDEC, Ludhiana, Punjab GNDEC, Ludhiana, Punjab sherry2009in@gmail.com sumeetksehra@gmail.com sukhjitsehra@gmail.com Abstract- In this paper an overview of Bee Colony Optimization and area of its application where it has been used is given. Bee Colony Optimization is based on concept of Swarm Intelligence (SI), the artificial intelligence (AI) which is based on decentralized and self- organizing systems that can either be natural or artificial. Bee Colony Optimization is a meta- heuristic algorithm which uses the swarm behavior of bees to interact locally with one another in their environment that simulates the foraging behavior of honey bees and combines the global explorative search with local explorative search. The Bees Algorithms hunts synchronously the most promising regions of the solution space and also samples the most favorable regions. BCO is a class of optimization algorithm which uses the bottom-up approach of modeling and swarm intelligence of honeybees. The primary aim of this paper is to give an insight into the areas in which BCO can be used. Keywords- Bee Colony Optimization (BCO), Swarm Intelligence, Bee Swarming, Artificial Intelligence, Collective Intelligence I. INTRODUCTION The term swarm is used for an aggregation of animals such as fish schools, bird’s flocks and colonies of insects like, ants, termites and colonies of bee performing collective behavior. The individual agent of a swarm behaves without surveillance and in a stochastic manner due to her perception in the neighborhood [7]. Swarm Intelligence is well known for its collective behavior which is basically decentralized and based on self-organized systems. Swarm Intelligence is basically a population based system that contains large number of agents in it that interact locally with one another and with their environment. This motivation generally arises from biological systems (natural occurring systems are colonies of bees, ants and fish etc.) These agents within the system follow very simple rules as no centralized control is available to tell them how to interact with each other. So such type of interaction leads to emergence of “ingenious” comprehensive global behavior which is not known to individual agents. This emergence of “collective intelligence” is known as Swarm Intelligence. The characteristics of self-organization of swarms are given as [14]: 1. Positive feedback is a simple “thumb rules” that helps in the creation of convenient structures. Recruitment and reinforcement such as dances in bees represented as examples of positive feedback. 2. Negative feedback compensates positive feedback and stabilizes the collective pattern. It is used to bypass the saturation situation which may occur in food sources or available foragers etc. 3. Fluctuations such as random walks, errors, random task switching among swarm individuals are crucial for creativity and innovation. Randomness is often crucial for emergent structures since it enables discovery of new solutions. 4. Multiple interactions occur between agents so the information from one another can be used and transmitted to whole network. The “Swarm intelligence” is used to describe the way how colonies of insects collectively contribute to solve a problem, which is known as “collective intelligence". It also describes the communication of social insects (bees, wasps, ants, and termites) which is based on their swarm behavior. The communication between them is well known and follows same set of rules. This communication between insects contributes to configuration of “collective intelligence” of social insect colonies. That’s why; the term “collective intelligence” came into existence. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO) was identified by Lucic and Teodorovic in 2001 [2]. The BCO approach is a “bottom-up” approach to modeling where special kinds of species i.e. artificial agents are formed by relation and analogy with bees. Artificial bee agents collaboratively solve complex combinatorial optimization problem [9]. Bee Colony Optimization is typical example of Swarm Intelligence. Bees are social insects that usually live together in large and well-organized family groups. In this paper