Particle Swarm Optimization And Application To Electrolytic Zinc Process Power Supply System

Researchers have focused on developing novel intelligence optimization methods to address the complexity, constraint, nonlinearity, multiple local minimum as well as the modeling difficulties in lots of scientific and engineering problems. Swarm intelligence optimization technique is such an algorithm developed from mimicking social behaviour of animals in the natural environment. The algorithm can be used to solve complicated optimization problem, without requiring centralized control and global modeling.In this dissertation, focus is on particle swarm optimization (PSO), one branch in the swarm optimization. Given the limitations of the existing PSO and its general application fields, several approaches are proposed to revise and improve the existing techniques. Applications of the revised techniques in scientific research and engineering problems are investigated. The research in this dissertation is to improve the existing PSO such that it can effectively solve the problems including neural network training and optimization of complex multimodal problems. The main contributions of the dissertation include:1. A Multi-Species Cooperative PSO (MCPSO) algorithm is developed using the ideas of species dividing in the bionomical world. The algorithm is shown to have guaranteed convergence to the global optimum. Furthermore, the training strategies of RBF network structures and weights based on the MCPSO aigorithm is proposed. Simulation results show that the algorithm is effective in such fields as chaotic time-sequence forecasting, system identification, speech signal processing and etc.2. To overcome the limitations of the existing multi-model function optimization PSO algorithms, a clustering based niching particle swarm optimization (CBNPSO) is constructed by combining a "density based" clustering method with NichePSO. The proposed algorithm incorporates two methods for the niching techniques: first, different algorithms are used for the evolvement of main swarms and sub-swarms; different sub-swarm with a main swarm is distinguished using clustering method.3. Applications of PSO in clustering analysis are investigated in the dissertation. An approach combining PSO with the mountain clustering method is proposed, which can overcome the limitations of the available fuzzy C-Means clustering and fuzzy C-Means clustering algorithm including demanding computations, difficulty parameter identifications and large errors in the resulting clustering centers. (1) A PSO-based mountain clustering algorithm is obtained combining the 1-best PSO of varying parameters with the mountain clustering method; (2) in simplifying computation of mountain clustering functions, PSO-based accelerated mountain clustering method is proposed; (3) combining the niche-PSO with the mountain clustering method, a PSO based mountain clustering algorithm is developed, which can automatically search for the number and locations of unknown multidimensional sample data.4. A Stochastic Multi-objective PSO (SMOPSO) is proposed by studying the key technique of using PSO to multi-objective problems, and then the convergence of SMOPSO is analyzed by using correlative theory of homogeneous Markov chains. Simulating test indicate that the proposed algorithm is computing simple and can obtain more homogenized pareto solutions.5. A multi-objective optimal model of time sharing power supply dispatching of electrolytic zinc is established based on the published studies, and using integral SMOPSO optimize the dispatching of time-sharing power supply for electrolytic zinc process. The proposed multi-objective PSO conquered the disadvantage of no transcendent knowledge to select the penitentiary coefficient, searching inefficient by using simulated annealing algorithm to optimize the dispatching of time-sharing power supply for electrolytic zinc process. The dispatching of time-sharing power supply for electrolytic zinc process not only conquers the cities lacking in electric power but also increases economic benefits of the corporation.6. A direct current power supply system in non-ferrous metallurgical industry is investigated. A model for optimization of the direct current power supply system is obtained. Based on the model, a Hierarchy multi-objective Particle Swarm Optimization (HMPSO) is developed, which can be used to optimize the system and achieve the economic operation of the system. With the objective of increasing the commuting efficiency, the algorithm can ensure to maintain the precision of output current, optimize the operation and current distributions of multiple parallel rectifier sets, and improve the commuting efficiency. The study advances a new idea and technique for economy of energy in non-ferrous metallurgical industry.7. An intelligent optimization and monitoring system is developed for a zinc electrolysis direct electric current supply process. Based the developed system the so-called "four-remote" function including remote metering, remote communication, remote control, and remote adjustment is implemented, and then the current supply substation is unmanned, running optimized and centralizing monitored. This system can advance the automatization and intelligence in non-ferrous metallurgical industry.Finally, the research results are summarized. Future work of PSO algorithm and its applications in the optimization and control of complicated industrial systems are discussed.