类搜索算法

提出利用类结构驱动的群体进化计算方法——类搜索算法(CSA).CSA在个体间构造簇类形态的虚拟连接关系,并通过对类组织的结构和类搜索过程进行动态调节来优化模拟进化系统的计算状态,提高群体的搜索效率.介绍了CSA的基本模型,并基于CSA融合进化算子与差分计算机制设计出数值优化算法CSA/DE.对多个典型高纬函数和复杂混合函数的仿真实验结果说明,CSA/DE是一种对高纬连续问题高效、稳定的搜索优化方法.该工作一方面验证了CSA的可行性和有效性;另一方面则显示:基于类搜索模型可有效融合异构且具有不同计算特性的搜索机制,形成对待求解问题更具针对性且协调性更佳的搜索计算方法.这为高性能优化算法的设计提供了一条新的途径.

Clustering Search Algorithm

A novel evolutionary optimization method, clustering searching algorithm (CSA), is presented. In CSA, a virtual cluster group is constructed among individuals in order to adjust the operation state of simulated evolutionary system dynamically and improve the searching efficiency of population. After introducing the basic model of CSA, this paper presents CSA/DE, a new CSA blending the evolutionary search operators with the differential computing mechanism for solving numerical optimization problem. In simulations, 6 classical multidimensional functions and 6 challenging composition functions are selected to test the performance of CSA/DE. The experimental results show CSA/DE is an efficient and reliable search optimization algorithm for multidimensional continuous problem. The work of this paper verifies the feasibility and validity of CSA. Meanwhile, this research demonstrates, based on CSA, multiple heterogeneous searching mechanisms can be merged into one algorithm to get much more pertinence and harmony in searching process, thus providing a viable way for designing high-performance optimization algorithm.