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- Title
Particle swarm optimization algorithm of learning factors and time factor adjusting to weights.
- Authors
MA Guo-qing; LI Rui-feng; LIU Li
- Abstract
Concerning the problem that the independent adjusting strategy of inertia weight and learning factor reduces evolution uniformity of particle swarm optimization (PSO) algorithm, and cannot adapt to the complex nonlinear optimization problems, this paper proposed a new PSO algorithm with learning factor controlled by inertia weight function. This strategy could effectively enhance the interaction of inertia weight and learning factor so as to balance the global exploration and local exploitation and preferably lead particles to search globally optimal solution. Based on that, it introduced the time factor, which treated as a linear function of inertia weight, in order to further improve the local development ability and convergence speed of iteration in the late. Aiming at the conflict between the convergence and the diversity of PSO, it proposed an strategy of boundary restrictions and speed rebound, which could avoid particles flying off resulting in a decrease of species diversity and made particles converge to global optimum quickly. The optimization analysis on benchmark test functions and the comparison with other PSO algorithm indicates that the algorithm balances individual and colonial learning ability of particles and improves optimization ability and the accuracy of convergence.
- Subjects
PARTICLE swarm optimization; COMPUTER algorithms; MACHINE learning; TIME factors (Learning); EVOLUTIONARY theories; ITERATIVE methods (Mathematics); STOCHASTIC convergence
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2014, Vol 31, Issue 11, p3291
- ISSN
1001-3695
- Publication type
Article
- DOI
10.3969/j.issn.1001-3695.2014.11.021