Research On Applications Of Particle Swarm Optimization To Industry Engineering Problems

Artificial intelligence optimization techniques, abbreviated as AIOT, are booming dramatically with the fast development and extensive application of computer hardware together with software. Many researchers and engineers has introduced artificial intelligence optimization technique (AIOT) into the field of engineering optimization to solve and optimize complex industrial production process in order to obtain both the maximum economic efficiency and social benefits. Lots of research and practice have proved that production efficiency and economic benefits can be significantly increased through the application of optimization techniques including the reasonable allocation of resources and energy consumption reductionSince many engineering problems can be induced as an optimization problem under certain mathematical model, highly efficienct optimization algorithem greatly influence the quality of the solution. From a great variety of practical industrial applications, artificial intelligence optimization has been proved to be an efficient optimization tool over conventional optimization methods.By now, AIOT has played a very important role not only in all the aspects of social area but also in the industrial and agricultural production departments, e.g., structural optimization in designing mechanical systems, computer based image processing, optimization of planning and scheduling system in process industry, transportation scheduling optimization, optimization of resource configuration and territorial development.The idea of Particle Swarm Optimization (PSO) is derived from the observation of bird fish and flocks movments. PSO is a novel swarm intelligence optimization algorithm and thus boosts the new branch of computational intelliegence. It is chiefly characterized by its simplicity of implementation, fast converfence speed and few parameters to manipulation. Its emergency has provoked a wide range of responses in the field of academic world and indutrial application. This dissertation mainly concerns on the applicaton of PSO algorithm in the area of modern industry and performance enhancement in the solution of nonlinear equation system, multimodal optimization, route optimization and location optimization.The research work mainly consists of three parts and is organized by the sequence of general description of artificial intelligence optimization technique (AIOT), problem demonstration, and presenting algorithm application. Research contents in details are shown as followings:(1) Origination, development history and main subfields of AIO technique are treated minutely in this part. First, a brief review of AIO, including the concept and initiation, is introduced and its prominent advantage is also described in the filed of numeric optimization. Then, three categories of AIO are presented, namely artificial neural network, evolutionary computation, and fuzzy system, respectively. Furthermore, genetic algorithms in evolutionary computation area, particle swarm optimization in swarm intelligence emphasized. Then successful applications of PSO in different engineering areas are summarized(2) Solution to the noneliear equation systems has always been the common obstacle in scientific research and engineering application. How to obtain a satisfactory solution to large quantities of noneliear equation in a reasonable computation time plays the key role in the process of texture analysis based on maximum entropy method. As all the solution variables exits on the exponential form, mishandling to them could results in the "overflow" phenomenon. On the one hand, classic numerical solution to such problem requires the equation to satisfay characteristic. On the other hand, initial iterative value is also difficult to determine before optimizing process by classic method. This research develops an stochastical and parallel searching PSO algorithm to solve such optimization problems and provide a robust computation technique for maximum entropy method in texure analysis.(3) Defect dection in printed circuit board industry is the major reseach problem in quality control procedure.Based on machine vision detecting method, this paper proposes a multi template matching method to locate multiple components with multi-direction, and the problem is transformed to a multimodal function optimization problem. Then various strategies embedding in evolutionary algorithms for solving multimodal problems are compared and analyzed. The newly developed species based particle swarm optimization is introduced in the PCB inspection procedure. In order to improve the inspection efficiency, three acceleration strategies are proposed. The three methods are called NCC-MTM store table, re-initialization procedure, and local search respectively. From experimental test, the acceleration strategies are proved to be efficient in locating multi components. Finally, a comparison of GA-MTM and Species-PSO are conducted to show that Species-PSO exceeds GA-MTM in computation time.(4) A balanced assignment multiple travelling salesman model is proposed to solve the production plan problem of hot rolling slab batch scheduling in steel company. First, a brief introduction to TSP and MTSP problem and corresponding solutions are demonstrated, and then four MTSP models are presented. A specific MTSP model which requires balanced workload is presented and a two stage solution method is introduced to solve the balance problem. Finally, simulation verification is performed by using TSPLIB data.Location problem is a widely studied combination optimization problem. This work first reviews the history of location problem and then introduces major variants of such problems. The model and various solution techniques for uncapacitated facility location problem are emphasized in description. As the computer hardware is upgrading fast, how to fully utilize the super performance of modern multi-core processor is a big problem with high priority for scientific researchers. This dissertation proposed OpenMP based parallzation method for solving UFLP by a multi swarm particle swarm optimization. Compared with serial executive PSO, the parallelized OpenMP based multi swarm optimization excels in computation time especially in larger scale benchmark problem.