摘要
针对基本果蝇优化算法收敛速度慢、求解精度低、易于陷入局部极值以及算法候选解不能取负值等不足,提出一种用于解决约束优化问题的改进果蝇优化算法.该算法利用果蝇个体历史最佳记忆信息和种群全局历史最佳记忆信息构建多策略混合协同进化的搜索机制,以达到有效平衡算法的全局探索与局部开发的目的,同时也能够较好地避免算法的早熟收敛问题;通过种群最优信息的实时动态更新和局部深度搜索策略的引入,进一步提高该算法的收敛速度和收敛精度.采用13个基准测试函数和2个工程优化问题来验证所提出算法的可行性与有效性,仿真实验结果表明,与其他典型智能优化算法相比,所提出的优化算法具有全局搜索能力强、稳定性好、收敛速度快、收敛精度高等优势,可有效解决复杂的约束优化问题.
In view of the shortcomings of the fruit fly optimization algorithm(FOA),such as slow convergence speed,low accuracy,easy to fall into local optimum,and the candidate solutions of the algorithm cannot take negative values,an improved fruit fly optimization algorithm(IFOA)for solving constrained optimization problems is proposed.Taking advantage of the best memory information of individual history and group global history,a multi-strategy hybrid co-evolutionary search mechanism is constructed,which can effectively balance the global exploration and local exploitation of the IFOA,and the premature convergence of the algorithm can also be better avoided.By introducing a real-time dynamic update mechanism and a local depth search strategy,the convergence speed and precision of the IFOA are further improved.The 13 benchmark problems and 2 engineering optimization problems are used to test the feasibility and effectiveness of the proposed method.Numerical results show that the proposed IFOA has obvious advantages such as stronger global search ability,better stability,faster convergence speed and higher convergence accuracy and so on,which can be used to effectively solve complex constrained optimization problems.
作者
石建平
李培生
刘国平
刘鹏
SHI Jian-ping;LI Pei-shengy;LIU Guo-ping;LIU Peng(School of Mechanical&Electrical Engineering,Nanchang University,Nanchang 330031,China;School of Electronic&Communication Engineering,Guiyang University,Guiyang 550005,China;School of Gems and Materials Technology,Hebei GEO University,Shijiazhuang 050031,China)
出处
《控制与决策》
EI
CSCD
北大核心
2021年第2期314-324,共11页
Control and Decision
基金
国家自然科学基金项目(51566012)
贵州省联合基金项目(黔科合LH字[2015]7302号).
关键词
果蝇优化算法
约束优化问题
协同进化
局部搜索
测试函数
工程优化
fruit fly optimization algorithm
constrained optimization problem
co-evolutionary
local search
benchmark function
engineering optimization
