Research On Intelligent Particle Swarm Optimization Algorithm

With the development of science and technology,the production process in national economy and defense science and technology field needs optimization design. Especially there will be a large number of high, precise, advanced products in aeronautics and astronautics fields in great need of more ascendant optimization methods. Particle Swarm Optimization (PSO) algorithm has the advantages of simple theory, less parameters, easy realization and quick convergence. Meanwhile it has the widespread adaptivity for different types of functions and has been applied in many fields.However, because PSO algorithm is a new arisen intelligent algorithm, it has the drawbacks of easy premature in initial iteration stages, and slowed-down converging speed in final stages and so on. Furthermore, the application expansion of PSO algorithm depends more on specific problems. Therefore this thesis mainly focuses on the deep theoretical analysis on PSO algorithm. Aiming at solving continuous and combination optimization problems, this thesis discussed the improving strategies to overcome the above drawbacks, put forwards a series of new algorithms and applied them into specific engineering practices.In terms of single-objective continuous function optimization, this thesis introduces the multi-swarm idea into PSO algorithm and put forward a two-layer multi-swarm particle swarm algorithm. This algorithm realized the swarm size expansion and the dual parallel-running mechanism, so it can purposefully enhance the algorithm global search ability. Meanwhile the different granularity multi-subswarms parallel mechanism and dual direction optimal information flow between subswarms also increases the algorithm local search ability. Tests on multimode and fraudulent typical functions verified the effectiveness of the above algorithm. An application Example of robot structure parameters optimization is further completed to testified its feasibility.Theory analysis and simulation tests in this paper show that the slow updating rate of individual best is one main cause for the slow converging speed in the later iteration stage of PSO algorithm. Therefore this thesis put forwards a vector position selecting-best updating PSO algorithm. In this algorithm, the position of each particle can be updated in a selectable way, namely each particle can select the best one from three candidate points as the new position, which can heighten the probability of finding much better position and increase the updating rate of individual best and global best. It consequently raises the algorithm performance with less computing time cost. By means of six test functions and an application example of mobile robot path planning, the feasibility and effectiveness of the algorithm is verified.Multi-objective constrained optimization has still been the bottle-neck problem in optimization field. Besides handling multiple objectives, it also has the problem that its constraint handling will affect the computing efficiency. PSO algorithm only keeps the optimal information and lacks of the intelligent judgment and reservation mechanism for the unfeasible solutions adjacent to the optimum,so it cannot get a satisfactory results in multi-objective optimization. Cultural algorithms are particularly fit for the constrained optimization problem because of its culture-based dual parallel mechanism of belief space and population space. Therefore this thesis put forward a dual level evolutionary cultural particle swarm algorithm for multi-objective constrained optimization problems. This new algorithm takes the improved particle swarm algorithm as its population space. It uses a direct comparison method to handle constraint conditions which can avoid the demerits of traditional penalty function method. Moreover, an adjustable parameter is regulated in a real-time way during the iteration process to keep unfeasible solutions within a certain proportion range and to maintain the diversity of the whole swarm. Therefore the evolutionary process in population space can avoid the premature problem and increase the global search ability. The belief space accepts the elitist particles from the population space. A crossover operation and niche Pareto competition strategy are further executed to ensure that the optimal set can be distributed uniformly on the Pareto frontier. Two test functions and an application example of engineering damper optimization design are finally presented to verify this algorithm,test and experiment results show this algorithm is a quick and effective multi-objective optimization method.Combination optimization problem has many extensive application backgrounds, and its objective is to find the optimal combinations of discrete status in the solution space. The discrete particle swarm algorithm presents a new solution for this kind of problems. This thesis considered the prime discrete particle swarm disadvantages of easily trapped local optimum and low solution precision, and put forward two specific new improved algorithms as follows.Taking the typical 0-1 knapsack problem as the research objective, this thesis put forwards a new virus co-evolutionary particle swarm algorithm for single objective combination problem based on bio-virus mechanism and infection based operation between host and virus. This algorithm utilizes the virus horizontal infection and vertical propagation ability to enhance its performance. Experiments show that the virus infection operation strengthens the local search ability in the solution space and the solving precision obviously outperforms several other algorithms.Taking the partner selection problem in virtual enterprise as the application background, this thesis discuss to apply discrete particle swarm algorithm into multi-objective combination optimization field. A new speed threshold adjustable discrete particle swarm algorithm is put forward based changing particle speed. By means of an adaptively adjustable speed threshold parameter to decrease with the iteration process, this algorithm can balance the contradiction between global search and local search, so it can heighten the computing performance.