Abstract
In response to the issues of premature convergence and instability of the phasor particle swarm optimization (PPSO) for solving function optimization problems, a new hybrid algorithm called bacteria PPSO (BPPSO) was proposed which combines the chemotaxis operation of the bacterial foraging optimization (BFO) algorithm with PPSO. In BPPSO, all individuals undergo tumbling and swimming strategies when the chemotaxis condition is met. New coefficients are introduced to update the positions of particles in BPPSO, achieving complementary advantages of BFO and PPSO. Finally, BPPSO is validated using eight benchmark functions, demonstrating its fast convergence speed, high computational accuracy, and good stability, making it a powerful global optimization algorithm.
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Liu, X., Wu, C., Chen, P., Wang, Y. (2023). Hybrid Algorithm Based on Phasor Particle Swarm Optimization and Bacterial Foraging Optimization. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_11
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DOI: https://doi.org/10.1007/978-3-031-36622-2_11
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