系统工程与电子技术 ›› 2018, Vol. 40 ›› Issue (3): 710-716.doi: 10.3969/j.issn.1001-506X.2018.03.34

• 软件、算法与仿真 • 上一篇    

求解大规模多背包问题的高级人工鱼群算法

李迎1, 张璟2, 刘庆1, 张伟3   

  1. 1. 西安理工大学自动化与信息工程学院, 陕西 西安 710048; 2. 西安理工大学计算机科学与工程学院, 陕西 西安 710048; 3. 北京天云融创软件技术有限公司, 陕西 西安 710075
  • 出版日期:2018-02-26 发布日期:2018-02-26

Advanced artificial fish swarm algorithm for large scale multiple knapsack problem

LI Ying1, ZHANG Jing2, LIU Qing1, ZHANG Wei3   

  1. 1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China; 2. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China; 3. China Sky Cloud Software Co. Ltd, Xi’an 710075, China
  • Online:2018-02-26 Published:2018-02-26

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摘要：

针对复杂的大规模多背包问题,提出了一种基于高级人工鱼群算法的求解方法。为了解决人工鱼群算法收敛速度慢、求解精度低的问题,所提算法通过改进其初始化方法,优化人工鱼个体的行为选择方式和追尾行为来加快问题求解的收敛速度;同时引入了动态视野及步长和人工鱼调整策略来提高算法搜索的精度。仿真实验表明:与现有的算法相比,所提算法不仅能快速收敛,而且可以达到更高的精度,尤其是对于规模越大的多背包问题算法性能提升越明显。

Abstract:

To solve the complicated large scale multiple knapsack problem, an advanced artificial fish swarm algorithm is proposed. In order to solve low convergence efficiency and accuracy of the artificial fish swarm algorithm, the improved initialization method, following behavior and behavior strategy are applied in the proposed algorithm to accelerate the convergence. Moreover, the dynamic visual and step setting and the artificial fish adjustment strategy are introduced to increase the searching accuracy. Experimental results show that the convergence efficiency and accuracy of the proposed algorithm are better than several existing algorithms, and the performance improvements are more significant with the increasing scale of the multiple knapsack problem.

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