ACM Computing Surveys (CSUR)
Swarm intelligence
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
The differential ant-stigmergy algorithm applied to dynamic optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A dynamic artificial immune algorithm applied to challenging benchmarking problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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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.