基于模拟退火高斯扰动的蝙蝠优化算法被引量：48

Bat algorithm based on simulated annealing and Gaussian perturbations

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摘要蝙蝠算法(bat algorithm,BA)是一类新型的搜索全局最优解的随机优化技术。为了提高BA算法的搜索效果,把模拟退火的思想引入到蝙蝠优化算法中,并对蝙蝠算法的某些个体进行高斯扰动,提出了一种基于模拟退火的高斯扰动蝙蝠优化算法(SAGBA)。分别将蝙蝠优化算法、模拟退火粒子群算法、SAGBA在20个典型的基准测试函数中进行仿真对比,结果表明SAGBA不仅增加了全局收敛性,而且在收敛速度和精度方面均优于其他两种算法。 Abstract： Bat algorithm is a new stochastic optimization technique for global optimization. This paper introduced both simula- ted annealing and Gaussian perturbations into the standard bat algorithm so as to enhance its search performance. As a result, it proposed a simulated annealing gaussian bat algorithm （SAGBA） for global optimization. It used BA, SAPSO and SAGBA to carry out numerical experiments for 20 test benchmarks. The simulation results show that the proposed SAGBA can indeed improve the global convergence. In addition, SAGBA is superior to the other two algorithms in terms of convergence and accu- racy.

作者贺兴时丁文静杨新社

机构地区西安工程大学理学院密德萨斯大学科学与技术学院

出处《计算机应用研究》 CSCD 北大核心 2014年第2期392-397,共6页 Application Research of Computers

基金陕西省软科学基金资助项目(2012KRM58) 陕西省教育厅自然科学基金资助项目(12JK0744 11JK0188) 西安工程大学研究生创新基金资助项目(chx131115)

关键词蝙蝠算法模拟退火高斯扰动仿真优化 bat algorithm（BA） simulate annealing（SA） Gaussian perturbations simulation optimization

分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TP301.6 [自动化与计算机技术—计算机系统结构]

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