Genetic Algorithm with Particle Filter for Dynamic Optimization Problems
The optimization problem that the optimum is timechanging by following a motion law in the search space is a dynamic optimization problem. This paper introduces the optimums motion information to the proposed algorithms. Particle Filter is used to predict and track the changing optima. In real solution space, GAs chromosome is the same as Particle Filters particle, both of which can be regarded as candidate solution. It is convenient to exchange information from the both. Two algorithms are designed to introduce the predicted particles of Particle Filter to genetic algorithm. The predicted particles serve as good genetic materials for GA in dealing with dynamic optimization problem and the optima which GA obtains in the stationary phase are viewed as observations to system state for Particle Filter. Both Particle Filter and genetic algorithm form the feedback loop and enhance the proposed algorithms ability of tracking the optimum. Experimental study over DF1 benchmark dynamic problem shows that the algorithms have good performance.
Dynamic optimization Genetic algorithm Particle Filter Prediction Feedback
Li Chen Lixin Ding Xin Du
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China Department of Early War State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China Software Academy, Fujian Normal University , Fuzhou, China
国际会议
上海
英文
452-457
2011-03-11（万方平台首次上网日期，不代表论文的发表时间）