摘要
针对非线性约束优化问题,运用了一种新的智能优化算法——社会认知优化算法。社会认知优化算法是一种基于社会认知理论的集群智能优化算法,它对目标函数的解析性质没有要求,适合于大规模约束问题处理的优点,使搜索不容易陷入局部最优。将该算法引入非线性约束问题,解决优化问题。通过实例和其他算法进行比较,对比数值实验结果表明,即使只有一个学习主体,该算法能够高效、稳定地得到解决方案,便于求解非线性约束优化问题。
This paper presents a new evolutionary algorithm for solving nonlinear constrained optimization problems based on Social Cognitive Optimization（SCO）.The SCO is a simple behavioral model based on human social cognition,it shows that it can get high quality solutions efficiently,even only one learning agent,which may be conveniently employed to execute random and global search.The SCO method is employed to conduct nonlinear constrained optimization problems.The experiments by comparing SCO with other algorithms on some functions show that it can get high quality solutions efficiently,even by only one learning agent.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第19期198-200,242,共4页
Computer Engineering and Applications
基金
国家科技攻关计划支撑项目(No.2006BAJ02B0803)
陕西省科技厅软科学基金资助项目(No.2008KR115)~~
关键词
社会认知算法
非线性约束优化
智能优化算法
社会认知理论
Social Cognitive Optimization（SCO）
nonlinear constrained optimization
intelligent optimization algorithm
social cognitive theory
