Abstract
The Cooperative Particle Swarm Optimization (CPSO) is a variant of the original PSO. It divides the solution vector into sub-vectors. Aiming to the stagnation problem of CPSO, this paper presents an improved cooperative particle swarm optimization algorithm (ICPSO). In order to retain the diversity of the swarm, it employs a comprehensive learning strategy to determine the position and velocity of each particle. It adds a factor of selection probability and discourages premature convergence to some extent. Through standard job-shop scheduling problem test, we demonstrate that the improved CPSO algorithm has an improvement in performance over the traditional CPSO. It has not only quicker speed of convergence but also less makespan.
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Zheng, Y., Qu, J., Wang, L. (2015). An Improved Cooperative PSO Algorithm for Job-Shop Scheduling Problem. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_22
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DOI: https://doi.org/10.1007/978-3-319-15554-8_22
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