Particle Swarm Optimization Study For Solving Constrained Optimization Problems

With fast development of technology, new arithmetic appears such as Genetic Algorithm, Ant Colony Algorithm, Evolutionary Programming, Evolutionary Strategies and Particle Swarm Optimization. Particle Swarm Optimization as a new Evolvement Algorithm receives a lot of attention for its characteristic of simpleness, less parameters and independence of the function. In recent years, more and more relevant study and achievements have come forth all over the world.In this thesis, an improved Particle Swarm Optimization Algorithm combined with Constrained Conditions was presented. New algorithms have three parts including individual part, social part and oriented part different from the used Particle Swarm Optimization Arithmetic. The accessorial part can make the improved algorithm reach the aim quickly and avoid to plunging local best and infinite circle.The thesis is arranged as follows: Paper summarizes the development of study firstly. Then it brings forward the new improved algorithm to treat with single Constrained Optimization Problems and Multi-objective Constrained Optimization Problems. Furthermore, via the designed scheme, it succeeds with dealing with army-transport path Optimization Problems. At last, it gives the expectation.