Abstract
The speed equation of particle swarm optimization is improved by using a convex combination of the current best position of a particle and the current best position which the whole particle swarm as well as the current position of the particle, so as to enhance global search capability of basic particle swarm optimization. Thus a new particle swarm optimization algorithm is proposed. Numerical experiments show that its computing time is short and its global search capability is powerful as well as its computing accuracy is high in compared with the basic PSO.
The work is supported by the National Natural Science Foundation of China under Grant No.60962006 and the Natural Science Foundation of Ningxia under Grant No.NZ0848.
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Gao, Y., Lei, F., Wang, M. (2010). A New Particle Swarm Optimization Algorithm and Its Numerical Analysis. 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_8
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DOI: https://doi.org/10.1007/978-3-642-13495-1_8
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