摘要
提出了一种新型的混合算法并命名为混合杂草蝙蝠算法(Hybridize Invasive Weed Optimization with Bat Algorithm,IWOBA),该算法在杂草算法的基础上利用蝙蝠算法的回声定位来解决每代种子逐步寻优的问题。其原理是利用种群速度和位置的不断更新,增加种群的多样性,从而达到提高种群的全局收敛性。最后利用6个测试函数对该算法和标准杂草算法进行测试比较。仿真结果表明,IWOBA能够有效克服原算法早熟、易陷入局部最优的缺点,可加快算法收敛速度,具有良好的鲁棒性。
A novel hybrid algorithm is proposed in this paper named hybridize invasive weed optimization with bat algorithm （IWOBA）. It is based on invasive weed optimization（IWO） and uses the echo-location Of bat algorithm to solve optimization problem of each seeds steps of steps. The principle is the constantly update of the speed and position of population, and increase the diversity of the population, to enhance the global convergence of population. Finally six test functions were used to validate the algorithm and the standard of invasive weed algorithm （IWO）. The simulation results indicate that the IWOBA can effectively overcome the shortcoming of local optimum and the original in puberty, and accelerate the algorithm convergence speed and have good robustness.
出处
《微型机与应用》
2015年第3期75-77,81,共4页
Microcomputer & Its Applications
关键词
杂草算法
蝙蝠算法
回声定位
invasive weed optimization algorithm
bat algorithm
echo-location
