Abstract—Many optimization problems in real world are dynamic in the sense that the global optimum value and the shape of fitness function may change with time. The task for the optimization algorithm in these environments is to find global optima quickly after the change in environment is detected. In this paper we describe a novel algorithm, which we have called DPSABC, and show that it can be applied to dynamic optimization problems. The core of this algorithm is using PSO to optimize the fitness value of population in ABC. Experimental results on various dynamic environments modeled by the moving peaks benchmark show that the proposed algorithm outperforms other algorithms, like mQSO, adaptive mQSO and RPSO.
Index Terms—Artificial Bee Colony, Particle Swarm Optimization, Dynamic Environment.
Noosheen Baktash is with the Department of Electrical and Computer Qazvin Branch Islamic Azad University, Qazvin, Iran
Mohammad Reza Meybodi is with the Department of Computer Engineering and Information Technology at Amirkabir University of Technology, Tehran, Iran.
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Cite: Noosheen Baktash and Mohammad Reza Meybodi, "A New Hybrid Model of PSO and ABC Algorithms for Optimization in Dynamic Environment,"
International Journal of Computer Theory and Engineering vol. 4, no. 3, pp. 362-364, 2012.
