Abstract
Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms’ performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO.
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Chai, QW., Pan, JS., Zheng, WM., Chu, SC. (2020). A Parallel Strategy Applied to APSO. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_7
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DOI: https://doi.org/10.1007/978-981-15-3308-2_7
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