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。
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.
出处
《计算机工程与科学》
CSCD
北大核心
2014年第7期1336-1340,共5页
Computer Engineering & Science
基金
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)
关键词
蝙蝠算法
量子行为
势阱
整数规划
bat algorithm
quantum-behaved
potential well
integer programming
