Abstract
The hybridization of meta-heuristics algorithms has achieved a remarkable improvement from the adaptation of dynamic parameterization. This paper proposes a variety of implementation frameworks for the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and the dynamic parameterization. In this paper, taxonomy of the PSO-GA with dynamic parameterization is presented to provide a common terminology and classification mechanisms. Based on the taxonomy, thirty implementation frameworks are possible to be adapted. Furthermore, different algorithms that used the implementation frameworks with sequential scheme and dynamic parameterizations approaches are tested in solving a facility layout problem. The results present the effectiveness of each tested algorithm in comparison to the single PSO and constant parameterization. Keywords : hybridization; PSO; GA; implementation frameworks; dynamic parameterization.
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