Abstract
Based on the analysis of biological symbiotic relationship, the mechanism of facultative parasitic behaviour is embedded into the particle swarm optimization (PSO) to construct a two-population PSO model called PSOPB, composed of the host and the parasites population. In this model, the two populations exchange particles according to the fitness sorted in a certain number of iterations. In order to embody the law of "survival of the fittest" in biological evolution, the poor fitness particles in the host population are eliminated, replaced by the re-initialization of the particles in order to maintain constant population size. The results of experiments of a set of 6 benchmark functions show that presented algorithm model has faster convergence rate and higher search accuracy compared with CPSO, PSOPC and PSO-LIW.
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Qin, Q., Li, R., Niu, B., Li, L. (2010). A New PSO Model Mimicking Bio-parasitic Behavior. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_9
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DOI: https://doi.org/10.1007/978-3-642-13495-1_9
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