| 用于函数优化的最大引力优化算法 |
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| 引用本文： | 金林鹏,李均利,魏平,陈刚.用于函数优化的最大引力优化算法[J].模式识别与人工智能,2010,23(5):653-662. |
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| 作者姓名： | 金林鹏 李均利 魏平 陈刚 |
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| 作者单位： | 宁波大学 信息科学与工程学院 宁波 315211 |
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| 基金项目： | 国家自然科学基金重点项目，浙江省自然科学基金项目，宁波市自然科学基金项目，宁波大学乇宽诚基金项目 |
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| 摘 要： | 提出一种基于牛顿万有引力定理的函数优化方法──最大引力优化算法。该算法通过“引力分组”和“引力淘汰”过程更新搜索体。文中给出4个引理来描述算法的数学基础，同时也给出算法的收敛性证明。此外还对该算法进行改进。最后与粒子群算法、差分算法、郭涛算法进行比较，数值结果显示该算法在解决连续函数优化问题具有较高的性能。
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| 关 键 词： | 函数优化 最大引力优化算法(MGOA) 模拟进化计算 万有引力 |
| 收稿时间： | 2009-04-13 |
Maximal Gravitation Optimization Algorithm for Function Optimization |
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| JIN Lin-Peng,LI Jun-Li,WEI Ping,CHEN Gang.Maximal Gravitation Optimization Algorithm for Function Optimization[J].Pattern Recognition and Artificial Intelligence,2010,23(5):653-662. |
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| Authors: | JIN Lin-Peng LI Jun-Li WEI Ping CHEN Gang |
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| Affiliation: | College of Information Science and Engineering,Ningbo University,Ningbo 315211 |
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| Abstract: | A global function optimization algorithm based on Newtons law of universal gravitation is proposed, namely maximal gravitation optimization algorithm (MGOA). The search agents are updated through the processes of gravitational clustering and gravitational elimination, which are two main strategies in MGOA. Four lemmas are provided to describe the mathematical foundation, and the convergence of MGOA is strictly proved. Furthermore, the proposed algorithm is improved. The experimental result shows MGOA has good performance in solving continuous function optimization problems, compared with some well-known heuristic search methods such as Particle Swarm Optimization, Differential Evolution, and Guo Tao algorithm. |
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| Keywords: | Function Optimization Maximal Gravitation Optimization Algorithm (MGOA) Simulated Evolution Computation Universal Gravitation |
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