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| 自适应混沌粒子群优化的粮食应急点选址研究 | |
| 引用本文： | 肖乐,吴相林,张雪萍. 自适应混沌粒子群优化的粮食应急点选址研究[J]. 河南工业大学学报(自然科学版), 2012, 33(4): 77-81 |
| 作者姓名： | 肖乐 吴相林 张雪萍 |
| 作者单位： | 1. 华中科技大学控制系,湖北武汉430074;河南工业大学信息科学与工程学院,河南郑州450001 2. 华中科技大学控制系,湖北武汉,430074 3. 河南工业大学信息科学与工程学院,河南郑州,450001 |
| 基金项目： | “十一五”国家科技支撑计划重点项目(2008BADA8B03);科技部科技型中小企业技术创新基金项目(10C26214102205) |
| 摘 要： | 针对粮食应急点选址,将“运输时间最小”和“应急开始最早”作为目标,建立了相应的优化模型.利用基于粒子群的K-Medoids聚类算法进行求解,为了避免过早地陷入局部最优,提出了自适应混沌粒子群优化算法.该算法利用粒子与已知全局最优粒子的欧式距离来判断粒子群当前状态,并将其作为确定混沌扰动范围的启发信息,可以有效地提高最优解的精度.试验表明该算法优于传统的演化算法,较好地解决了粮食应急点选址问题. |
| 关 键 词： | 粮食应急点 选址 聚类 自适应混沌粒子群算法 距离启发信息 |
RESEARCH ON SITE SELECTION OF GRAIN EMERGENCY SUPPLY LOCATION BASED ON SELF-ADAPTIVE CHAOS PARTICLE SWARM OPTIMIZATION |
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| XIAO Le , WU Xiang-lin , ZHANG Xue-ping. RESEARCH ON SITE SELECTION OF GRAIN EMERGENCY SUPPLY LOCATION BASED ON SELF-ADAPTIVE CHAOS PARTICLE SWARM OPTIMIZATION[J]. Journal of Henan University of Technology Natural Science Edition, 2012, 33(4): 77-81 | |
| Authors: | XIAO Le WU Xiang-lin ZHANG Xue-ping |
| Affiliation: | 1.Department.of Control Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;2.School of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China) |
| Abstract: | In order to solve the problem of site selection of grain emergency supply location,we established a corresponding optimization model to achieve the targets of the shortest transport time and the earliest emergency start time.We solved the model by use of a particle swarm-based k-medoids cluster algorithm,and we also put forward a self-adaptive chaos particle swarm optimization algorithm to avoid the solution from sinking to local optimum prematurely.The algorithm judged the current state of the particle swarm according to the Euclidean distance between particles and the known global optimum particle,and determined the chaos perturbation range by using the current state as the heuristic information so as to effectively improve accuracy of the optimal solution.The experiment showed that the algorithm was superior to the conventional evolutionary algorithm and could well solve the problem of site selection of grain emergency supply location. |
| Keywords: | grain emergency supply location site selection cluster self-adaptive chaos particle swarm algorithm distance heuristic information |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
