Artificial Bee Colony algorithm using Structured Swarm
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 7
Abstract
Swarm Intelligence is a meta-heuristic approach in the field of nature inspired techniques that is used to solve optimization problems. It is based on the collective behaviour of social creatures. Social creatures utilize their ability of social learning to solve complex tasks. The swarm intelligence based algorithms which have emerged in recent years includes ant colony optimization (ACO), particle swarm optimization (PSO), bacterial foraging optimization (BFO), artificial bee colony optimization (ABC) etc. In ABC each bee stores candidate solution and modifies its candidate over time stochastically, based on the best solution found by neighboring bees and based on the best solution found by the bee its own experience. When tested over various benchmark function and real life problems, it has performed better than a few evolutionary algorithms and other search heuristics. However ABC, like other probabilistic optimization algorithms, has inherent drawback of premature convergence or stagnation that leads to loss of exploration and exploitation capability. In recent years, many researchers focus and suggested various optimization algorithms based on swarm intelligence. Therefore; this report resented a modified ABC. In the proposed strategy, search process in ABC is performed by smaller group of independent swarms of bees. A new control parameter named perturbation rate (pr) also introduced in the employed bee phase which control the perturbation in the food positions explore by employed bees. The experiments show that the proposed strategy has better diversity and faster convergence than the basic ABC.
Authors and Affiliations
Rajesh Rajaan
Keywords
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