Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
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This paper proposes an information sharing model of artificial bee colony for locating multiple peaks in dynamic environments. The concept of niching is implemented by using a hybridized approach that combines a modified variant of the fitness sharing technique with a speciation based response to the changing environment. The informative dynamic niching bee colony algorithm helps to synchronize the employer and onlooker forager swarms by synergizing the local information with a modified perturbation strategy. This main crux of our algorithm is its independency of problem dependent control parameter, like niche radius, and the absence of any hard-partitioning clustering technique that leads to high computational burden. Our framework aims at bringing about a simple, robust approach that can be applied to a variety of problems. Experimental investigation is undertaken pertaining to the competitive performance of our algorithm with the existing techniques in order to highlight the significance of our work.