文献类型：期刊文献

中文题名：种群规模可变的免疫多模态函数优化

英文题名：Multi-model function optimization based on immune clonal optimization with self-adaptive population size

作者：张华伟[1];丁松阳[1]

第一作者：张华伟

机构：[1]河南财经政法大学计算机与信息工程学院

第一机构：河南财经政法大学计算机与信息工程学院

年份：2013

卷号：33

期号：3

起止页码：814-815

中文期刊名：计算机应用

外文期刊名：Journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：河南省教育厅自然科学研究项目(2A520004)

语种：中文

中文关键词：免疫优化;多模函数;种群规模;变化规则;局部搜索

外文关键词：immune optimization; multi-model function; population size; change rule; local search

摘要：为了平衡种群规模对算法搜索效率和有效性的影响,提出了一种种群规模可变的免疫克隆算法求解多模态函数优化问题。给出了种群规模的变化规则、变化幅度和变化的具体实现过程,实现了种群规模根据进化过程自适应的动态变化。此外,结合多模态函数优化的特点,采用Baldwin学习作为局部搜索机制,增强算法搜索最优解的能力。实验结果表明,本算法寻优能力较强,收敛速度较快,并且较为稳定。
In order to balance the impact of population size on the efficiency and effectiveness of the search algorithm, an immune clonal algorithm with self-adaptive population size for multi-modal function optimization problem was proposed. The change rules, magnitude and the detailed realization of population size were given. The change of self-adaptive population size with the evolutionary process was achieved. In addition, in terms of multi-modal function optimization characteristics, Baldwin learning was used as a local search strategy to enhance the search ability for the optimal solution. The experimental results show that the algorithm has better optimization ability, faster convergence and is more stable than existing algorithms.