It is inspired by the foraging behaviour of the honey bees. The hive sends out the Scout bees which when locate nectar (a sugary fluid secreted within flowers), return to the hive and communicate the other bees the fitness, the quality, distance and direction of the food source via waggle dance. The objective of the algorithm is to locate and explore good sites within a problem search space. Many scout bees are sent out; each iteration is always in search of additional good sites which are continually exploited in the local search application. In computer science and operations research, the bee’s algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies. The bees algorithm mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of n agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness). The bees’ algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number T of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.

Journal of Swarm Intelligence and Evolutionary Computation provides an International forum for the publication of papers in the following areas. Artificial Intelligence; Robotics; Modelling & Analysis of swarm particle optimization; Swarm Intelligence; Evolutionary programming & Evolutionary Genetics; Genetic Algorithm & Genetic Programming; Ant colony Optimization; Bacterial Forging; Artificial Life & Digital Organisms; Bioinformatics; Evolutionary Computation; Artificial Immune System; Computing; Nano computing; Computational intelligence, etc. Swarm Intelligence journals are at higher echelons that enhance the intelligence and information dissemination on topics closely related to Swarm Intelligence. Computational methods in synthetic biology play a major role in this journal.

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