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| 基于集合的细菌群优化算法 | |
| 引用本文： | 许鑫,刘衍珩,王爱民,陈慧灵,孙鑫.基于集合的细菌群优化算法[J].吉林大学学报(工学版),2012,42(6):1491-1497. |
| 作者姓名： | 许鑫 刘衍珩 王爱民 陈慧灵 孙鑫 |
| 作者单位： | 1. 吉林大学计算机科学与技术学院,长春,130012 2. 吉林大学计算机科学与技术学院,长春130012／吉林大学符号计算与知识工程教育部重点实验室,长春130012 3. 吉林大学计算机科学与技术学院,长春130012／温州大学物理与电子信息工程学院,浙江温州325035 |
| 基金项目： | 国家自然科学基金项目(60973136,61073164) |
| 摘 要： | 针对细菌觅食优化算法求解高维优化问题时不易跳出局部最优解的问题,引入趋向方向余弦向量和随时间变化的加速系数,控制细菌觅食优化算法的收敛精度和收敛速度,并将改进算法用于求解组合优化问题。依据细菌种群密度计算原则,设计了一种离散空间和连续空间之间相互转换的规则,同时用集合对细菌觅食优化算法中的算术运算符形式化描述。仿真试验结果表明:基于集合的细菌群优化算法避免了早熟现象,寻优结果优于蚁群算法且接近基于集合的粒子群算法。 |
| 关 键 词： | 人工智能 组合优化问题 离散空间 细菌觅食优化算法 细菌群优化算法 |
Optimization algorithm of bacterial swarm based on the collection |
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| XU Xin,LIU Yan-heng,WANG Ai-min,CHEN Hui-ling,SUN Xin.Optimization algorithm of bacterial swarm based on the collection[J].Journal of Jilin University:Eng and Technol Ed,2012,42(6):1491-1497. | |
| Authors: | XU Xin LIU Yan-heng WANG Ai-min CHEN Hui-ling SUN Xin |
| Affiliation: | 1(1.College of Computer Science and Technology,Jilin University,Changchun 130012,China;2.Symbol Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;3.College of Physics and Electronic Information,Wenzhou University,Wenzhou 325035;China) |
| Abstract: | The conventional Bacterial Foraging Optimizer(BFO) is easy to get stuck in local minima in solving high dimensional optimization problems.This paper proposes an adaptive Bacterial Swarm Optimizer(BSO) with time-varying acceleration coefficients to solve the local-optimal problems of BFO.The proposed method,termed as ABSO-TVAC,is applied to optimize Combinatorial Optimization Problems(COPs).When dealing with COPs,according to the cell-to-cell signaling in E.coli swarm,a conversion rule between continuous space and discrete one is designed.All arithmetic operators in the velocity and position updating rules used in the ABSO-TVAC are described in a manner of set.Simulation shows that the proposed algorithm has the capability to avoid the premature problem,and it is superior to Ant Colony Optimization(ACO) and comparable to set-based particle swarm optimization. |
| Keywords: | artificial intelligence combinatorial optimization problems discrete space bacterial foraging optimizer optimization algorithm of bacterial swarm |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |