一种群体智能融合算法及其在应急设施选址的应用

J4 ›› 2014, Vol. 36 ›› Issue (04): 667-673.

一种群体智能融合算法及其在应急设施选址的应用

许骏,许晓东

（华中科技大学公共管理学院，湖北武汉 430074）

收稿日期:2012-10-08 修回日期:2013-03-05 出版日期:2014-04-25 发布日期:2014-04-25

A fusion algorithm of swarm intelligence and
its application in emergency services facility location

XU Jun,XU Xiaodong

(School of Public Administration,Huazhong University of Science and Technology,Wuhan 430074,China)

Received:2012-10-08 Revised:2013-03-05 Online:2014-04-25 Published:2014-04-25

摘要/Abstract

摘要：

针对粒子群优化算法早熟及细菌觅食算法收敛慢的问题，提出了将量子粒子群优化与细菌觅食算法融合的一种群体智能融合算法。该算法将细菌觅食、量子计算理论及粒子群优化的优点进行融合，以细菌觅食算法为主体，将量子进化算法及粒子群优化算法嵌入其中，从而极大地提高了算法的性能。通过对三个标准函数求解和验证，结果表明该算法提高了收敛精度及速度。最后用该算法求解公共卫生应急服务设施点选址问题，取得了较好的效果，说明了该算法的有效性。

关键词: 群体智能算法, 量子粒子群优化, 细菌觅食算法, 应急选址

Abstract:

In order to solve the problem of prematurity of swarm intelligence algorithm and slow speed of convergence of bacterial forging algorithm, an algorithm is proposed, which fuses quantum particle swarm together with bacteria foraging optimization. After considering the advantages of bacteria foraging optimization, quantum computing theory and particle swarm optimization, the proposed fusion algorithm takes the bacterial foraging algorithm as the main body and embedded quantum evolutionary algorithm and particle swarm optimization algorithm into it, thus improving the performance of the algorithm greatly. Through solving and validating the three criteria functions, the results show that the proposed fusion algorithm improves convergence precision and speed. Finally, the proposed algorithm is used to solve public health emergency service facility location problem and achieves good results, proving its effectiveness.

Key words: swarm intelligence algorithm;quantum particle swarm optimization;bacterial foraging algorithm;emergency service facilities point location

许骏,许晓东. 一种群体智能融合算法及其在应急设施选址的应用[J]. J4, 2014, 36(04): 667-673.

XU Jun,XU Xiaodong. A fusion algorithm of swarm intelligence and
its application in emergency services facility location [J]. J4, 2014, 36(04): 667-673.

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