A Clustering Particle Based Artificial Bee Colony Algorithm for Dynamic Environment
Modern day real world applications present us challenging instances where the system needs to adapt to a changing environment without any sacrifice in its optimality. This led researchers to lay the foundations of dynamic problems in the field of optimization. Literature shows different approaches undertaken to tackle the problem of dynamic environment including techniques like diversity scheme, memory, multi-population scheme etc. In this paper we have proposed a hybrid scheme by combining k-means clustering technique with modified Artificial Bee Colony (ABC) algorithm as the base optimizer and it is expected that the clusters locate the optima in the problem. Experimental benchmark set that appeared in IEEE CEC 2009 has been used as test-bed and our ClPABC (Clustering Particle ABC) algorithm is compared against 4 state-of-the-art algorithms. The results show the superiority of our ClPABC approach on dynamic environment.
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