Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduc...

For every quality software product it is important that its quality be improved and maintenance time be reduced as the product ages. The product should be reliable and should meet the standards as well. To do so proper testing strategies must be in place and they should be executed religiously. Here we recommend a algorithm on the principles of Artificial Bee Colony. Here the Bee is supposed to...

A wide range of engineering design problems have been solved by the algorithms that simulates collective intelligence in swarms of birds or insects. The Artificial Bee Colony or ABC is one of the recent additions to the class of swarm intelligence based algorithms that mimics the foraging behavior of honey bees. ABC consists of three groups of bees namely employed, onlooker and scout bees. In A...

BACKGROUND Recent elevated winter loss of honey bee colonies is a major concern. The presence of the mite Varroa destructor in colonies places an important pressure on bee health. V. destructor shortens the lifespan of individual bees, while long lifespan during winter is a primary requirement to survive until the next spring. We investigated in two subsequent years the effects of different lev...

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimizat...

We explore self-organizing strategies for role assignment in a foraging task carried out by a colony of artificial agents. Our strategies are inspired by various mechanisms of division of labor (polyethism) observed in eusocial insects like ants, termites, or bees. Specifically we instantiate models of caste polyethism and age or temporal polyethism to evaluated the benefits to foraging in a dy...

The artificial bee colony optimization (ABC) is a population-based algorithm for function optimization that is inspired by the foraging behavior of bees. The population consists of two types of artificial bees: employed bees (EBs) which scout for new, good solutions and onlooker bees (OBs) that search in the neighborhood of solutions found by the EBs. In this paper we study in detail the influe...

In the basic Artificial Bee Colony (ABC) algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to whic...