摘要
提出一种基于Alopex的进化算法.该算法在迭代过程中从种群中随机选择两个个体,通过计算两个个体自变量和目标函数值的变化情况确定算法进一步搜索方向的概率,逐步迭代最终收敛到全局最优.该算法具备基本进化算法和Alopex算法的优点,在一定程度上具有梯度下降法和模拟退火算法的优点.通过基准函数的测试和反应动力学参数估计的应用表明,该算法的全局搜索能力有了显著提高,特别是对多峰函数能够有效避免早熟收敛问题.
An Alopex based evolutionary algorithm is proposed. Its salient feature is randomly selecting two individuals and computing their objective values. According to the information of the two individuals, the probability of search direction is ascertained. By iterative computing, the global optimum is obtained. It has the advantages of both gradient methods and simulation anneal algorithm to some extent. The anneal temperature is self-adjusting over the proceeding of evolution. The proposed algorithm is used to optimize the benchmark functions and the kinetic parameters of 2-ehlorophenol oxidation in supercritical water. The experimental results demonstrate that the proposed algorithm is superior to the original evolutionary algorithms, especially for the multi-apices function problems.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第3期452-456,共5页
Pattern Recognition and Artificial Intelligence
基金
国家863计划资助项目(2007AA04Z171)
关键词
进化算法
模拟退火
函数优化
Evolutionary Algorithm, Simulated Anneal, Function Optimization
