Abstract
Population Diversity in Particle Swarm Optimization (DPSO) algorithm can effectively balance the “exploration” and “exploitation” ability of the PSO optimization algorithm and improve the optimization accuracy and stability of standard PSO algorithm. However, the accuracy of DPSO for solving the multi peak function will be obviously decreased. To solve the problem, we introduce the linearly decreasing inertia weight strategy and the adaptively changing inertia weight strategy to dynamically change inertia weight of the DPSO algorithms and propose two kinds of the improved DPSO algorithms: linearly decreasing inertia weight of DPSO (Linearly-Weight- Diversity-PSO, LWDPSO) and adaptively changing inertia weight of DPSO (Adaptively-Weight-Diversity-PSO, AWDPSO). Three representative benchmark test functions are used to test and compare proposed methods, which are LWDPSO and AWDPSO, with state-of-the-art approaches. Experimental results show that proposed methods can provide the higher optimization accuracy and much faster convergence speed.
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Xin, J., Yan, C., Han, X. (2015). Improved DPSO Algorithm with Dynamically Changing Inertia Weight. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_13
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DOI: https://doi.org/10.1007/978-3-319-20466-6_13
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