An improved particle swarm optimization algorithm

Yan Jiang

doi:10.1016/j.amc.2007.03.047

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An improved particle swarm optimization algorithm

2007-10-01

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蒋燕 (Yan Jiang) *

雷

雷晓辉 (Xiaohui Lei)

DOI：10.1016/j.amc.2007.03.047

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Abstract

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An improved particle swarm optimization (IPSO) is proposed in this paper. In the new algorithm, a population of points sampled randomly from the feasible space. Then the population is partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization (PSO) algorithm. At periodic stages in the evolution, the entire population is shuffled, and then points are reassigned to sub-swarms to ensure information sharing. This method greatly elevates the ability of exploration and exploitation. Simulations for three benchmark test functions show that IPSO possesses better ability to find the global optimum than that of the standard PSO algorithm. Compared with PSO, IPSO is also applied to identify the hydrologic model. The results show that IPSO remarkably improves the calculation accuracy and is an effective global optimization to calibrate hydrologic model. (C) 2007 Elsevier Inc. All rights reserved.

Keywords:

particle swarm optimization

improved particle swarm optimization

global optimization

hydrologic model

parameters calibration