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- W4285356075 abstract "Particle Swarm Optimization (PSO) is an evolutionary algorithm for solving global optimization problems. In recent two decades, a lot of researches have been working on improving the performance of PSO and numerous PSO variants have been presented. According to a recent theory, the hardest problem to one evolutionary algorithm may be the easiest to another algorithm, and vice versa. Thus, mixed strategy PSO algorithms might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) was proposed which integrates five different PSO variants. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 variables. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than those of comparative algorithms." @default.
- W4285356075 created "2022-07-14" @default.
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- W4285356075 date "2021-10-01" @default.
- W4285356075 modified "2023-09-27" @default.
- W4285356075 title "Mixed Strategy PSO for Global Optimization" @default.
- W4285356075 doi "https://doi.org/10.1109/sagc52752.2021.00027" @default.
- W4285356075 hasPublicationYear "2021" @default.
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