Abstract
A new variant of particle swarm optimization, Logistic Dynamic Particle Swarm Optimization (termed LDPSO), is introduced in this paper. LDPSO is constructed based on the new inspiration of population generation method according to the historical information about particles. It has a better searching capability in comparison to the canonical method. Furthermore, according to the characteristics of LDPSO, two improvement strategies are designed respectively. Mutation strategy is employed to prevent premature convergence of particles. Selection strategy is adopted to maintain the diversity of particles. Experiment results demonstrate the efficiency of LDPSO and the effectiveness of the two improvement strategies.
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Ni, Q., Deng, J. (2011). Two Improvement Strategies for Logistic Dynamic Particle Swarm Optimization. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_33
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DOI: https://doi.org/10.1007/978-3-642-20282-7_33
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