协调探索和开发能力的改进灰狼优化算法
发布时间：2018-09-07 22:01
【摘要】：提出一种协调探索和开发能力的灰狼优化算法.利用佳点集方法初始化灰狼个体的位置,为全局搜索多样性奠定基础;为协调算法的全局探索和局部开发能力,给出一种基于正切三角函数描述的非线性动态变化控制参数;为加快算法的收敛速度,受粒子群优化算法个体记忆功能的启发,设计一种新的个体位置更新公式.10个标准函数的测试结果表明,改进灰狼优化(IGWO)算法能够有效地协调其对问题搜索空间的探索和开发能力.
[Abstract]:A grey wolf optimization algorithm is proposed to coordinate exploration and development. The optimal point set method is used to initialize the individual position of the gray wolf, which lays the foundation for global search for diversity, and provides a nonlinear dynamic change control parameter based on tangent trigonometric function for coordinating the global exploration and local development of the algorithm. In order to speed up the convergence of the algorithm and inspired by the individual memory function of the particle swarm optimization algorithm, a new updating formula of individual position is designed. The test results of 10 standard functions show that, The improved gray wolf optimization (IGWO) algorithm can effectively coordinate its ability to explore and develop the search space of the problem.
【作者单位】： 贵州财经大学贵州省经济系统仿真重点实验室;贵州财经大学数学与统计学院;湖南人文科技学院能源与机电工程学院;
【基金】：国家自然科学基金项目(61463009) 商务部与贵州财经大学联合基金项目(2016SWBZD13) 贵州省科学技术基金项目(黔科合基础[2016]1022) 湖南省自然科学基金项目(2016JJ3079) 湖南省教育厅青年基金项目(14B097)
【分类号】：TP18
，
本文编号：2229535
[Abstract]:A grey wolf optimization algorithm is proposed to coordinate exploration and development. The optimal point set method is used to initialize the individual position of the gray wolf, which lays the foundation for global search for diversity, and provides a nonlinear dynamic change control parameter based on tangent trigonometric function for coordinating the global exploration and local development of the algorithm. In order to speed up the convergence of the algorithm and inspired by the individual memory function of the particle swarm optimization algorithm, a new updating formula of individual position is designed. The test results of 10 standard functions show that, The improved gray wolf optimization (IGWO) algorithm can effectively coordinate its ability to explore and develop the search space of the problem.
【作者单位】： 贵州财经大学贵州省经济系统仿真重点实验室;贵州财经大学数学与统计学院;湖南人文科技学院能源与机电工程学院;
【基金】：国家自然科学基金项目(61463009) 商务部与贵州财经大学联合基金项目(2016SWBZD13) 贵州省科学技术基金项目(黔科合基础[2016]1022) 湖南省自然科学基金项目(2016JJ3079) 湖南省教育厅青年基金项目(14B097)
【分类号】：TP18
，
本文编号：2229535
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