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Research on Particle Swarm Optimization and Its Applications

Author: ZhouChi
Tutor: GaoLiang
School: Huazhong University of Science and Technology
Course: Industrial Engineering
Keywords: Particle Swarm Optimization Neural Network Constrained Optimization Information-sharing mechanisms Shop scheduling
CLC: TP301.6
Type: Master's thesis
Year: 2007
Downloads: 559
Quote: 5
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Abstract


Currently, by simulating the behavior of biological communities to solve computational problems has become a new hotspot, and formed the core of the theory of swarm intelligence system. Particle swarm optimization algorithm is based on the theory of swarm intelligence optimization algorithm. The algorithm uses the biological communities within the cooperation and competition between individuals and other complex behavior of swarm intelligence, and for engineering optimization problems provide efficient solutions. This paper studies the particle swarm optimization algorithm to train the neural network, constrained optimization, discrete combinatorial optimization applications, and gives more than the prospect of engineering applications. First, the system introduces the particle swarm optimization algorithm, summed up its development process in a variety of improved model, summed up the basic particle swarm optimization applications, and an overview of its application in the field of engineering optimization. Secondly, the study of particle swarm optimization training in the application of neural networks is proposed based on particle swarm optimization neural network training algorithm-SPSO. The algorithm in the training of the neural network while optimizing its connection structure, delete redundant connections, partly eliminates redundant parameters of the neural network performance, so neural network pattern classification problems obtaining information processing capabilities to match. The algorithm used to train the neural network prediction and classification applied to water quality and credit assessment and training is given with other commonly used algorithms compare the results. Then, the study of particle swarm optimization in power system constraints such as allocation and tolerance allocation problems in the application. Proposed for the mechanism of particle swarm optimization constraint handling strategies, and through mixing with the direct search algorithm, particle swarm optimization to enhance the ability of local search, and finally by an example to verify the effectiveness of the method. The method can be used to solve constrained optimization can be attributed to non-linear programming engineering optimization problems. Finally, through in-depth analysis of particle swarm optimization mechanism, breaking the traditional speed - displacement search model, a generalized model of particle swarm optimization. Analysis of the traditional PSO limitations of information sharing mechanism, a new population-based information-sharing mechanisms. Will be based on the information sharing mechanism algorithm is applied to permutation flow shop scheduling problem and open-shop scheduling problem, the experimental results demonstrate the effectiveness of information sharing mechanism and superiority.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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