摘要
在蝙蝠算法优化的研究中,为提高蝙蝠算法的种群多样性和局部搜索性能,受病毒进化机制启发,提出一种改进的蝙蝠算法:病毒进化蝙蝠算法。采用病毒群体感染主群体,主群体在历代个体间纵向传递信息以利于全局优化,病毒群体通过感染操作在同代个体间横向传递信息利于局部搜索。利用多个不同规模的算例进行对比仿真,结果表明所提算法的在寻优精度和求解稳定性方面优于量子遗传算法和二进制粒子群等算法,可用于有效求解背包问题等NP难问题。
In order to enhance the diversity of the population and the global search ability, bat algorithm was im- proved with inspiration from the virus evolutionary mechanism in the paper. The virus evolutionary bat algorithm was proposed to solve knapsack problem. Specifically, the main groups which consist of bats transmit information crossed the vertical generations and guided the global search, while the virus groups transfer evolutionary information crossed the same generation through virus infection and guide the local search. Comparison experiments on several instances with different sizes were conducted, and the results show that the proposed algorithm can effectively solve knapsack problems and achieve better result than binary bat algorithm, quantum genetic algorithm and binary particle swarm optimization in terms of computational accuracy and robustness.
出处
《计算机仿真》
CSCD
北大核心
2015年第6期256-261,共6页
Computer Simulation
基金
陕西省重点学科专项(E08001)
陕西省教育厅科研计划项目(2013JK0185)
西安建筑科技大学人才科技基金(RC1324)
关键词
蝙蝠算法
病毒进化
组合优化
背包问题
Bat algorithm
Virus evolution
Combinatorial optimization
Knapsack problem
