Abstract
Sharing mechanism is introduced into particle swarm optimization. Fitness values of particles are updated to sharing fitness values. Particles with higher sharing fitness value are punished and particles with smaller sharing fitness value are remained as memory particles. Particles are updated with memory particles and clone selection when global best have not changed in some continuous generations. Population diversity is increased by this way. At the same time the particle with the best fitness value is saved. The modified algorithm can avoid the local optimization and has better search performance to multi-peak functions. The experimental results show the modified algorithm has better convergence performance than standard particle swarm optimization algorithm.
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Hu, C., Zeng, J., Jie, J. (2007). Immune Particle Swarm Optimization Based on Sharing Mechanism. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_26
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DOI: https://doi.org/10.1007/978-3-540-74769-7_26
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