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Hybrid Radius Particle Swarm Optimization Applications

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Computer and Information Science (ICIS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 849))

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Abstract

The Hybrid Radius Particle Swarm Optimization (HRPSO) algorithm is developed, where the social interaction among the agent particle over the radius of swarm circle topology is applied to avoid premature convergence. For the optimization benchmark functions, HRPSO performs better than Particle Swarm Optimization (PSO) and other existing methods. The HRPSO is applied to solve several real-world optimization problems, including the Resource-Constrained Project Scheduling Problem (RCPSP) and Travelling Salesman Problem (TSP). We have designed and investigated different approaches, such as adaptive mutation, forward backward propagation, and k-means combined with the Radius Particle Swarm Optimization (RPSO) to solve these problems. The efficiency of the proposed method is tested against the existing methods. The results show that the HRPSO gives better optimum results.

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Mahanakorn University of Technology, Bangkok, 10530, Thailand

    Mudarmeen Munlin

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  1. Mudarmeen Munlin
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Correspondence to Mudarmeen Munlin .

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  1. Software Engineering and Information Technology Institute, Central Michigan University, Mount Pleasant, MI, USA

    Roger Lee

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Munlin, M. (2020). Hybrid Radius Particle Swarm Optimization Applications. In: Lee, R. (eds) Computer and Information Science. ICIS 2019. Studies in Computational Intelligence, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-030-25213-7_12

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