Research On Hybrid Particle Swarm Cooperative Optimization Algorithm And Its Application  Posted on:20101114  Degree:Doctor  Type:Dissertation  Country:China  Candidate:S L Song  Full Text:PDF  GTID:1118360275486767  Subject:Control theory and control engineering  Abstract/Summary:  PDF Full Text Request  In science and technology research, a lot of computing problems can be formulated as a global optimization problem of the objective function with nonlinear and multipeak characteristics. How to solve a global solution of these problems is one of the most important topics in optimization. Recently, particle swarm optimization (PSO) algorithm is one of the most powerful methods for solving such problems, compared with other optimization algorithms, its advantages highlighted. Although PSO has gained much attention and wide applications in different fields, there are still slower local convergence and lower computational accuracy problem caused by premature convergence, and how to improve the globally convergence ability has been the main research direction so far.In this paper, based on indepth study of particle swarm optimization algorithm, from start to upgrade its computing performance, a number of improved particle swarm optimization algorithms were given, the corresponding measure has been higher results; Under the guidance of thought of the hybrid algorithm, hybrid particle swarm cooperative optimization algorithms were proposed and achieved very good computing performance and results. The difficult problems of the material balance computation in the alumina production process were solved accurately and efficiently. Several points are included in this paper as follows:The origin and background of PSO were introduced, and the current research and application situations were summarized deeply, and then structural characteristics and calculation process of the standard PSO algorithm were carried out the necessary analysis of the process.It is reasonable that improving exploration ability can make particles explore solution space more efficiently in order to improve PSO global performance, the concepts of the gathering degree and the steady degree were defined, then three different improved PSO algorithms were proposed. First is a novel particle swarm optimization algorithm based on adaptive space mutation (SMPSO). During the searching process, the convergence speed and globally convergence ability is greatly improved by the adaptive space mutation based on the variance of the population's fitness, the convergence theorem of the algorithm is proved. Second is a particle swarm optimization algorithm with accelerating factor (AFPSO). The particle can dynamically adjust flying velocity according to flying direction at the different iterations, and effectively escapes from local optimum solution according to the gathering degree and the steady degree, and finally attains global optimum solution, and then the convergence and parameter selection of the algorithm are analyzed and discussed deeply. Third is a novel particle swarm optimization with stochastic mutation (AMPSO). The mutation probability of the current particle is determined by the mean of all the particle's fitness, the gathering degree and the steady degree, the exploration ability is efficiently improved by the mutation, and the probability of falling into local optimum is greatly decreased. Experimental results and practical application in quality monitoring of laser welding process show the new methods have faster convergence speed and higher globally convergence ability than the standard PSO.For complex optimization problems, the abilities of exploration and exploitation of algorithm often cannot be utilized and balanced effectively by depending solely on one method, thus influence solving overall precision and efficiency of the algorithm. Based on the main framework of particle swarm optimization algorithm, we proposed two hybrid particle swarm cooperative optimization algorithms in combination with certain excellent characteristics and mechanisms of other optimization algorithms. They retain the original advantages of particle swarm optimization, the disadvantages were offset by the merits of other algorithms, such as sudden jump characteristics of simulated annealing, fast convergence of the simplex algorithm, and randomness and ergodicity of the strong chaotic motion. Simulation testing and engineering application results show that hybrid particle swarm cooperative optimization algorithms have a more comprehensive optimization performance. The paper discusses the methods and steps of the integration problem of other algorithms and particle swarm optimization, and the convergence theorems of hybrid algorithms were proved.Based on the indepth analysis and discussion the technological process and the basic principles of the alumina production process in Bayer, three mathematical models of Bayer material balance computation were proposed for practical engineering applications in different production technology, complex engineering computing problems were first converted into nonlinear multiobjective optimization problems, and the above optimization algorithms were applied to solve them and achieved excellent results. The results also show that two hybrid optimization algorithms have more comprehensive computing performance than the improved optimization algorithms.Finally, the whole research contents were summarized, particle swarm optimization algorithm was prospected for the direction of development in future.  Keywords/Search Tags:  Particle Swarm Optimization, Cooperative Optimization, Swarm Intelligence, Algorithm Convergence, Space Mutation, Accelerating Factor, Stochastic Mutation, Material Balance Computation  PDF Full Text Request  Related items 
 
