Research On Particle Swarm Optimization And Its Several Applications In Engineering

Particle Swarm Optimization (PSO) algorithm is based on swarm intelligence theory. The algorithm can provide efficient solutions for optimization problems through intelligence generated from complex activities such as cooperation and competition among individuals in the biologic colony. This dissertation mainly focuses on PSO based algorithm and its potential engineering applications such as neural network training, cutting parameter optimization, travel salesman problem and job shop scheduling problem.Firstly, particle swarm optimization is introduced and its developments are reviewed. The basic applications of PSO algorithm and its engineering applications are summarized. The future research directions of PSO algorithm are pointed out and its potential applications are proposed.Secondly, Structure-improving Particle Swarm Optimization (SPSO) algorithm for training artificial neural network (ANN) is proposed. By tuning the structure and connection weights of ANN simultaneously, the proposed algorithm eliminates some ill effects introduced by redundant input features and the corresponding redundant structure of ANN, and obtains optimized ANN with problem-matched capacity for information processing.Thirdly, particle swarm optimization based algorithm is used to optimize cutting parameters. Constraint handling strategy suit for PSO mechanism is proposed. Furthermore, through combination with direct search, the search ability of PSO algorithm in local region is intensified. The efficiency of this algorithm is validated when used to select cutting parameters for single-pass milling opearation. The proposed algorithm could be applied for any engineering optimization application that can be mathematically modelled as nonlinear programming problem.Fourthly, General Particle Swarm Optimization (GPSO) model and its application in travel salesman problem are studied. The limitation of traditional PSO model in discrete optimization and combinatorial optimization is discussed. A deep investigation on mechanism of PSO is conducted. Then, not limited to the traditional velocity-position model of PSO algorithm, GPSO model is proposed and the corresponding algorithm for travel salesman problem is put forward. Experimental results show its validity.Finally, particle swarm optimization based job shop scheduling is studied. The limitation of information sharing mechanism of GPSO model is discussed. Based on it, improved information sharing mechanism for population based meta-heuristic algorithms is proposed. The new mechanism could easily be combined with specific population based meta-heuristic algorithm. Some benchmark JSP problems is used to validate its robustness.