| 空间优化选址中的增强型人工蜂群算法 |
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| 引用本文： | 张申利,王海起,龚 安. 空间优化选址中的增强型人工蜂群算法[J]. 计算机工程与应用, 2015, 51(15): 255-260 |
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| 作者姓名： | 张申利 王海起 龚 安 |
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| 作者单位： | 1.中国石油大学（华东） 计算机与通信工程学院，山东 青岛 266580 2.中国石油大学（华东） 地球科学与技术学院，山东 青岛 266580 |
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| 摘 要： | 公共服务设施选址是一种复杂的空间优化问题，选址的好坏关系到公共服务设施能否发挥其最大作用。利用穷举算法难以对高维的数据问题进行求解。针对空间优化选址的特点及人工蜂群算法收敛速度慢的问题，提出了适合空间选址的邻域搜索新公式，并将交叉的思想引入到了算法中，加快了全局最优解的寻优速度。对算法的可行性和有效性进行了验证，实验表明增强型人工蜂群算法比基本的人工蜂群算法取得了较优的效果。
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| 关 键 词： | 人工蜂群算法 空间优化选址 邻域搜索 交叉 |
Enhanced artificial bee colony algorithm in spatial optimal location |
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| ZHANG Shenli,WANG Haiqi,GONG An. Enhanced artificial bee colony algorithm in spatial optimal location[J]. Computer Engineering and Applications, 2015, 51(15): 255-260 |
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| Authors: | ZHANG Shenli WANG Haiqi GONG An |
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| Affiliation: | 1.School of Computer and Communication Engineering, China University of Petroleum（East China）, Qingdao, Shandong 266580, China 2.School of Earth Science and Technology, China University of Petroleum（East China）, Qingdao, Shandong 266580, China |
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| Abstract: | Public service location is a complex problem of spatial optimal location. The position quality has direct impact on whether the public services are able to maximize their effect. Exhaustive algorithm is hard to solve spatial optimal location with high-dimensional data. For space-optimized site characteristics and artificial bee colony algorithm slow convergence problem, a neighborhood searching formula for space-optimized site is proposed, and the crossing idea is introduced into the algorithm to speed up the global optimal solution optimization. The feasibility and effectiveness of the algorithm are verified. Experiments indicate that the enhanced artificial bee colony algorithm achieves better results than the basic artificial bee colony algorithm. |
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| Keywords: | artificial bee colony algorithm spatial optimal location neighborhood search crossing |
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