Abstract
This paper presents an Opposition-based PSO(OVCPSO) which uses Velocity Clamping to accelerate its convergence speed and to avoid premature convergence of algorithm. Probabilistic opposition-based learning for particles has been used in the proposed method which uses velocity clamping to control the speed and direction of particles. Experiments have been performed upon various well known benchmark optimization problems and results have shown that OVCPSO can deal with difficult unimodal and multimodal optimization problems efficiently and effectively. The numbers of function calls (NFC) are significantly less than other PSO variants i.e. basic PSO with inertia weight, PSO with inertia weight and velocity clamping (VCPSO) and opposition based PSO with Cauchy Mutation (OPSOCM).
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Shahzad, F., Baig, A.R., Masood, S., Kamran, M., Naveed, N. (2009). Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO). In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_34
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DOI: https://doi.org/10.1007/978-3-642-03156-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03155-7
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