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PSO in constrained optimization problems , and BP neural network applications

Author: LiaoFeng
Tutor: GaoXingBao
School: Shaanxi Normal University
Course: Computational Mathematics
Keywords: PSO Constrained optimization problem Premature convergence Differential evolution algorithm BP neural network
CLC: TP18
Type: Master's thesis
Year: 2008
Downloads: 373
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Abstract


PSO is typical of swarm intelligence optimization algorithm, due to its simple and easy to implement , fast convergence in engineering practice it has great potential , and is widely used in the objective function optimization, neural networks , combinatorial optimization, image processing and other fields. However, PSO exist for solving premature convergence and accuracy is not high shortcomings in order to overcome the shortcomings of premature convergence , this paper proposes two improved particle swarm algorithm and applied separately constrained optimization problem and BP neural network training Chapter 1 aim for solving constrained optimization problems first conventional numerical methods are reviewed , a brief introduction penalty method , barrier function method, feasible direction method and multiplier method and discusses these traditional algorithms for solving constrained optimization problem solving algorithm of the advantages and disadvantages as well as advantages of modern Secondly introduces the basics of BP neural network and BP algorithm in Chapter 2 from the PSO model parameters , status and other aspects of particle swarm algorithm is described in detail and lists PSO many examples of successful applications for the application of PSO algorithm to provide a reference depth in Chapter 3, the particle swarm algorithm for constrained optimization problems deal with the inevitable shortcomings will produce infeasible points , first proposed a deal with a new program infeasible points ; followed by the introduction differential evolution particle swarm optimization algorithm as the child presents a hybrid particle swarm algorithm algorithm ; Finally, numerical experiments show that hybrid particle swarm algorithm can effectively solve constrained optimization problems Chapter 4 presents a simple multi-swarm particle swarm algorithm, the algorithm various sub- populations in space independent search ; further , in order to avoid premature convergence algorithm , based on population fitness variance pairs each particle populations current optimal solution for local extreme disturbance , so as to enhance the global search ability . Finally, the simple particle swarm optimization for multi- group training BP neural networks, pattern recognition achieved good results.

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