摘要
针对传统差分进化算法在优化多维复杂函数时早熟收敛和收敛速度慢的问题,提出一种基于模式搜索的差分进化算法。在优化过程中加入判断个体早熟收敛的机制,若检测到有早熟现象,以当前种群搜索到的最优解作为有效初始点进行模式搜索,使算法跳出局部最优,增强全局寻优能力。采用典型的测试函数进行仿真,结果表明,与基本差分进化算法和基于混沌搜索的差分进化算法相比,该算法最易跳出局部最优解,收敛精度较高,优化性能较强。
In order to avoid premature convergence and slow convergence of the complex function optimization in traditional Differential Evolution （DE） algorithms, an improved DE algorithm based on pattern search is presented. A mechanism to judge premature convergence is introduced. If premature convergence is detected, this algorithm will regard the optimal solution of the current population as an effective initial point for pattern search to avoid local optimum and strengthen the global optimization ability. Several typical test functions are used in simulation, and results show that the proposed algorithm has stronger ability to jump out of local optimal solution, higher convergence precision and stronger optimization performance compared with the basic DE algorithm and the DE algorithm based on chaos search.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第9期163-167,共5页
Computer Engineering
基金
国家自然科学基金资助项目"进化计算类智能算法在数据同化误差处理中的应用研究"(41461078)
关键词
差分进化
模式搜索
函数优化
全局寻优
早熟收敛
收敛精度
Differential Evolution （DE）
pattern search
function optimization
global optimization
premature convergence
convergence precision
