基于Tent混沌搜索的差分进化算法及其应用  

Differential Evolution Algorithm Based on Tent Chaos Search and its Application

作　　者：邓泽喜[1] 刘晓冀[2] 

机构地区：[1]毕节学院数学与计算机科学学院,贵州毕节551700 [2]广西民族大学数学与计算机科学学院,广西南宁530006

出　　处：《计算机仿真》2013年第6期304-307,413,共5页Computer Simulation

基　　金：国家自然科学基金(11061005);贵州省教育厅自然科学基金资助项目(黔教科2010072)

摘　　要：针对差分进化算法求解函数优化问题存在过早收敛和不稳定等缺陷,提出一种利用Tent混沌搜索的差分进化算法(TCDE)。用Tent映射初始化种群,并以种群搜索到的最优个体为基础产生Tent混沌序列,以提高种群多样性,增强算法跳出局部最优解的能力。几个典型测试函数的测试结果表明TCDE的搜索能力优于DE。将改进算法应用于近似计算导数,仿真结果表明,新算法不仅能近似求解一阶导数,还能近似计算较复杂的高阶导数。For premature convergence and instability of differential evolution in solving function optimization problem, a differential evolution algorithm based on Tent chaos search （TCDE） was proposed. In order to improve the population＇s diversity and the ability of breaking away from the local optimum, the particles were generated by Tent mapping, and Tent chaotic sequence based on best individual were produced. Several Benchmark functions were test- ed and the results show that the proposed algorithm has better global convergence ability than that of DE. The im- proved algorithm was applied to approximate derivative and the results show that it can compute not only first deriva- tive, but also higher order derivative.

关 键 词：差分进化算法 混沌搜索 求导 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]