整数规划的量子行为蝙蝠算法

J4 ›› 2014, Vol. 36 ›› Issue (07): 1336-1340.

整数规划的量子行为蝙蝠算法

李枝勇,马良,张惠珍

(上海理工大学管理学院,上海 200093)

收稿日期:2012-11-13 修回日期:2013-04-12 出版日期:2014-07-25 发布日期:2014-07-25
基金资助:
国家自然科学基金资助项目(70871081);上海市一流学科建设项目资助（S1201YLXK）;上海市研究生创新基金资助项目(JWCXSL1202)；沪江基金资助项目（A14006）

A quantum-behaved bat algorithm for integer programming

LI Zhiyong,MA Liang,ZHANG Huizhen

(School of Management,University of Shanghai for Science and Technology,Shanghai 200093,China)

Received:2012-11-13 Revised:2013-04-12 Online:2014-07-25 Published:2014-07-25

摘要/Abstract

摘要：

蝙蝠算法是一种新型群体智能算法,传统的蝙蝠算法在解决整数规划问题时容易陷入局部最优并出现早熟收敛现象,为了解决这些弊端,提出了一种基于势阱的具有量子行为的蝙蝠算法。论述了算法的优化原理和实现方式,并通过仿真实验,与粒子群算法和量子行为粒子群算法进行性能对比。实验结果表明，量子行为蝙蝠算法不仅能够有效地解决整数规划问题,而且比其他算法具有更好的性能。

关键词: 蝙蝠算法, 量子行为, 势阱, 整数规划

Abstract:

The bat algorithm is a new type of the swarm intelligence algorithm. The traditional bat algorithm is easily trapped in the local optimum and has premature convergence in solving the integer programming problem. In order to solve the disadvantages, a quantumbehaved bat algorithm is proposed, which is based on the potential well. The principle of the optimization algorithm is discussed and its implementation is presented. The performance of the proposed quantumbehaved bat algorithm is compared with that of particle swarm optimization and quantumbehaved particle swarm optimization. The experimental results show that the proposal can handle integer programming efficiently and outperform other algorithms.

Key words: bat algorithm;quantum-behaved;potential well;integer programming

李枝勇,马良,张惠珍. 整数规划的量子行为蝙蝠算法[J]. J4, 2014, 36(07): 1336-1340.

LI Zhiyong,MA Liang,ZHANG Huizhen. A quantum-behaved bat algorithm for integer programming [J]. J4, 2014, 36(07): 1336-1340.

参考文献

［1］Ma Liang, Zhu Gang, Ning Aibing. Ant optimization algorithm［M］.Beijing:Science Press, 2008.(in Chinese)
［2］Liu Yong, Ma Liang. Bee colony foraging algorithm for integer programming［C］∥Proc of IEEE Business Management and Electronic Information (BMEI), 2011:199201.
［3］Omran M G H, Engelbrecht A, Salman A. Barebones particle swarm for integer programming problems［C］∥Proc of IEEE Swarm Intelligence Symposium, 2007:170175.
［4］Sun Jun, Fang Wei, Wu Xiaojun, et al. Quantumbehaved particle swarm optimization:Theory and application［M］. Beijing:Tsinghua University Press, 2011.(in Chinese)
［5］Gao Shang,Yang Jingyu.Ant colony optimization algorithm for nonlinear integer programming［J］. Journal of Nanjing University of Science and Technology, 2005, 29(suppl):120123.(in Chinese)
［6］Yang X S. A new metaheuristic batinspired algorithm［C］∥Proc of Nature Inspired Cooperative Strategies for Optimization(NICSO 2010), 2010:6574.
［7］Yang X S. Bat algorithm for multiobjective optimization［J］. International Journal of BioInspired Computation, 2011, 3(5):267274.
［8］Yang X S, Gandomi A H. Bat algorithm:A novel approach for global engineering optimization［J］. Engineering Computation, 2012, 29(5):267289.
［9］Gandomi A H, Yang X S, Alavi A H, et al. Bat algorithm for constrained optimization tasks ［J］. Neural Computing &Applications,2013,22(6):12391255.
［10］Sun Jun, Feng Bin, Xu Wenbo. Particle swarm optimization with particles having quantum behavior ［C］∥ Proc of 2004 Congress on Evolutionary Computation, 2004:325331.
附中文参考文献:
［1］马良,朱刚,宁爱兵.蚁群优化算法［M］.北京:科学出版社,2008.
［4］孙俊,方伟,吴小俊,等.量子行为粒子群优化:原理及其应用［M］.北京:清华大学出版社,2011.
［5］高尚,杨静宇.非线性整数规划的蚁群算法［J］.南京理工大学学报, 2005, 29(增刊):120123.

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