An effective DE algorithm with double populations to parameter estimation
Wang Peichong, Qian Xu, Fengjun Lei · 2013
Parameter estimation is an important problem in engineering and scientific research. To some systems,there is no way can be found to get the real values of their parameters. For some advantages including self-organize, developmental search, self-fitness etc, some intelligent algorithms have been given to solve these problems, such as GA, PSO, ACO etcs. Differntial Evolution is an effective tool for solving global optimization and has been used in some fields. This paper proposes a novel DE algorithm (DPDE) with double populations to solve parameter estimation problem. There are two populations in DPDE. Each population has its own evolutionary model and they finish evolution by different evolutionary model independently. Two Populations implement co-evolution based on local information transfer and share between populations. Result of experiments shows that DPDE is an effective and feasible method in solving parameter estimation problem.