Research On Modified Particle Swarm Optimization And Its Application

Particle Swarm Optimization (PSO) is a new biology -inspired algorithms, which based on swarm iterative steps. PSO emphasizes cooperation among individual particles to search the optimum location. Compared with GA and other biology-inspired algorithms, PSO has simpler principle, fewer parameters to adjust, so it has attracted increasing attention and been a new hotspot in the filed of artificial intelligence in the past decade. Since PSO algorithm has a short history, its theory and specific applications need further expansion. This thesis made an in-depth exploration on PSO from the aspects of search mechanism, modification and applications. This thesisis organized as follows:A modified particle swarm optimization is proposed to overcome the problems such as low precision that existed in standard PSO algorithm. On one hand this algorithm searches for the extreme value by tracking three extreme values (individual extreme value, global extreme value, circumference extreme value). Meanwhile, three non-linear functions are used to adjust the inertia weight. Complex function optimization problems are used to test the performance of the algorithm.A nonlinear diffusion particle swarm optimization (NDPSO) is proposed to improve the poor search quality of the standard PSO in high-dimensional function optimization. To avoid excessive diffusion operation in initial iterative stage, NDPSO introduces a nonlinear increasing mode into particle diffusion operation. Meanwhile, by bringing more opportunities to the later period of iterative process, this approach improves the global searching ability under the condition than the efficiency of PSO is not depressed. Moreover, a nonlinear function is introduced to adjust the inertia weight and enhance the searching ability when diffusion operation is not used. Simulations show that non-linear diffusion particle swarm optimization has outstanding performance in high-dimensional function optimization compared with traditional and other modified PSO algorithms.For discrete particle swarm algorithm, a discrete particle swarm optimization algorithm based on chaotic ant behavior is proposed. Inspired by ant colony algorithm, pheromone refresh mechanism of ant colony algorithm is introduced into the PSO to redefine the update equations of the speed and position, and chaos operation is utilized to re-initialize the population under certain conditions in this proposed algorithm. Then we used Knapsack problem to test the performance of the proposed algorithm. Compared with other algorithms, experiment results show that the proposed algorithm achieves more profits.A mobile robot artificial potential field global path planning approach based on modified particle swarm optimization algorithm is proposed. Dynamic diffusion particle swarm optimization algorithm (DDPSO) is used to choose the parameters of artificial potential field are optimized. By this approach, some objections of artificial potential field'model is solved, and then the route length and safety are optimized. Simulations indicate that it is effective to improve the performance of motion planning.