Optimizing complex functions by chaos search
Optimizing complex functions by chaos search
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DOI：
10.1080/019697298125678
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发表时间：
1998-06-01
影响因子：
1.7
通讯作者：
Jiang, WS
中科院分区：
文献类型：
--
作者：
Li, B;Jiang, WS
在过去的几十年中，优化的作用在许多领域稳步提升。它是控制理论研究中的一个热点问题。在实践中，优化问题变得越来越复杂。传统算法无法令人满意地解决这些问题，要么陷入局部极小值，要么需要更多的搜索时间。混沌常存在于非线性系统中，它具有许多良好的特性，如遍历性、随机性和“规律性”。混沌运动能够在某一区域内无重复地经过每一个状态。利用混沌的这些特性，提出了一种有效的优化方法：混沌优化算法（COA）。通过混沌搜索，一些复杂的优化问题得到了很好的解决。测试结果表明，COA的效率远高于一些常用于优化复杂问题的随机算法，如模拟退火算法（SAA）和趋化性算法（CA）。混沌优化方法为优化各类具有连续变量的复杂问题提供了一种新的高效途径。
During past decades, the role of optimization has steadily increased in many fields. It is a hot problem in research on control theory. In practice, optimization problems become more and more complex. Traditional algorithms cannot solve them satisfactorily. Either they are trapped to local minima or they need much more search time. Chaos often exists in nonlinear systems. It has many good properties such as ergodicity, stochastic properties, and "regularity." A chaotic motion can go nonrepeatedly through every state in a certain domain. By use of these properties of chaos, an effective optimization method is proposed: the chaos optimization algorithm (COA). With chaos search, some complex optimization problems are solved very well. The test results illustrate that the efficiency of COA is much higher than that of some stochastic algorithms such as the simulated annealing algorithm (SAA) and chemotaxis algorithm (CA), which are often used to optimize complex problems. The chaos optimization method provides a new and efficient way to optimize kinds of complex problems with continuous variables.