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- W2353901080 abstract "An Adaptive Evolutionary Particle Swarm Optimisation(AEPSO) approach was proposed to improve the performance of PSO algorithm for multi-objective optimisation.The proposed approach incorporated non-dominated sorting, adaptive inertia weight and a special mutation operation into particle swarm optimisation to enhance the exploratory capability of the algorithm and improve the diversity of the Pareto solutions.Instead of using weighted aggregation approach to deal with multi-objective optimisation problems, dominance-based rank was used to guide the flight of particles.The proposed algorithm was validated using several well-known benchmark test functions and successfully applied to the multi-objective optimal design of alloy steels, which aimed at determining the optimal process parameters and the required weight percentages of the chemical composites in order to obtain the pre-defined mechanical properties of the materials.The experimental results have shown that the algorithm can locate the constrained optimal design with good accuracy while keep the diversity of the Pareto solutions." @default.
- W2353901080 created "2016-06-24" @default.
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- W2353901080 date "2009-01-01" @default.
- W2353901080 modified "2023-09-25" @default.
- W2353901080 title "Adaptive Evolutionary Particle Swarm Algorithm for Multi-Objective Optimisation" @default.
- W2353901080 hasPublicationYear "2009" @default.
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