摘要
借鉴蚁群算法和惩罚函数的思想提出了一种用于求解连续空间约束优化问题的蚁群算法.应用自适应调整惩罚因子的惩罚函数法将约束优化问题转化为无约束优化问题,再结合自适应调整全局选择因子和信息素挥发系数的连续域蚁群算法,求解连续空间约束优化问题.通过对基准测试函数进行编程求解,对比采用固定参数的蚁群算法求解结果,验证了所提改进算法的正确性和有效性.
With ideas of ant colony algorithm and penalty function,an ant colony algorithm,which can solve continuous space constrained optimization problems,was proposed. We adopted the penalty function method of adjusting its value of adaptively to transform the constrained optimization problems into unconstrained optimization problems,and then combined with the continuous domain ant colony algorithm of adjusting its global selection factor and the value of the pheromone evaporation factor adaptively to solve the continuous space constrained optimization problems. And through programming solution of one benchmarking function,we compared the results with those of using fixed parameters ant colony algorithm,it was verified with correctness and effectiveness.
出处
《郑州大学学报（工学版）》
CAS
北大核心
2015年第1期20-23,共4页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(61075071)
河南省教育厅自然科学基金资助项目(14A413008)
郑州市科技局资助项目(131PPTGG409-5)
关键词
连续空间
约束优化
蚁群算法
惩罚函数
continuous space
constrained optimization
ant colony algorithm
penalty function method
