The Study Of Particle Swarm Optimization In Dynamic Environments

There are many optimization problems in the fields of industry, society, economy, management, and so on. As a new evolutionary technology, Swarm Intelligence Algorithm simulating some natural phenomenon, has made progresses on solving these optimization problems. As one of Swarm Intelligence Algorithm, Particle Swarm Optimization, with simple and programming easily, has already got the successful application in many realms.The PSO algorithm is already successfully applied in the optimization of various static functions. However, many real world problems are dynamic and stochastically change over time, the current reasonable optimum is not certainly the optimum in the next time. It is essential to re-design the model of problem over time. It is realistic and active meanings to track changes for dynamic environments and search the optimum after a change.In order to track the movement of optimum with changeable environments, the goal in dynamic environments is not only to detect the changes automatically but also to respond a variety of changes as timely as possible. From view of detection and response, a new improved and adaptive PSO has been introduced and discussed in detail in this paper. The main researches are as follows:1,An improved detection method at the particle level not only reduces the optimization cost but also makes up the limitation of the usual detection methods.2,Response conditions,including population diversity and Dgbest,are designed. The reasons as well as necessity of putting forward response conditions and relation with reset are analyzed.3,Motivated by birds"social cognition"from standard PSO model, a new response method, learning from the optimum for new environments, is designed. This method defines part of particles to be reset and their flying direction after a change.Finally, the developing and research trend of PSO in more complicated environments are pointed out.