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• W2734726414 abstract "Surrounded by an assortment of intelligent and efficient search entities, the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are proven to be a comprehensive tool for solving different kinds of optimization problems due to their contradictive working approaches. In addition, the two algorithms have achieved a remarkable improvement from the adaption of dynamic parameterizations. In this work, dynamic parameterized mutation and crossover are individually and in combination hybridized with a PSO implementation. The performances of different dynamic parameterizations of the hybrid algorithms in solving facility layout problem are compared with single PSO. The comparison revealed that the proposed technique is more effective." @default.
• W2734726414 created "2017-07-21" @default.
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• W2734726414 date "2017-05-01" @default.
• W2734726414 modified "2023-09-27" @default.
• W2734726414 title "Dynamic parameterizations of particle swarm optimization and genetic algorithm for facility layout problem" @default.
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