求解大规模优化问题的可全局收敛蝙蝠算法  

Bat algorithm with global convergence for solving large-scale optimization problem

作　　者：黄光球[1] 赵魏娟[1] 陆秋琴[1] 

机构地区：[1]西安建筑科技大学管理学院,西安710055

出　　处：《计算机应用研究》2013年第5期1323-1328,共6页Application Research of Computers

基　　金：陕西省科学技术研究发展计划项目(2011K06-08);陕西省教育厅科技计划项目(12JK0789)

摘　　要：为了快速求解大规模优化问题,构造出了可全局收敛的蝙蝠算法。在该算法中,将一个蝙蝠看成是优化问题的一个候选解;采用正交拉丁方原理生成蝙蝠群的初始空间位置,以达到对搜索空间的均衡分散性和整齐可比性覆盖;将蝙蝠的追随、自主、避险和从众行为用于构造每个蝙蝠的空间位置转移策略;利用蝙蝠捕获猎物时的响度和脉冲速率来确保整个蝙蝠群要么保持原状态,要么向好的空间位置转移,但绝不会向差的空间位置转移。在蝙蝠群移动过程中,蝙蝠从一个空间位置转移到另一个空间位置实现了蝙蝠群对优化问题最优解的搜索。结果表明,采用可归约随机矩阵稳定性定理可证明本算法具有全局收敛性。测试案例表明,本算法用于求解大规模优化问题时具有对不同类型优化问题适应性强且收敛速度快的优势。To solve large-scale optimization problems（ OP）, this paper constructed a bat ract：algorithm with global convergence. In the algorithm, each bat was just an alternative solution of OP, used the principle of orthogonal Latin squares to construct initial positions of bat group so as to cover search space with balance dispersion and neat comparability, used the following behavior, autonomous behavior, averting-danger behavior and conformability behavior of bats to construct space positions trasnsfering strategies;used the loudness and rate of plus emission to ensure the bat groups keep either to stay unchanged or to transfer to- ward better positions, but never to transfer worse positions. During evoluation process, a bat＇ s transferring from one position to another realized the search for the optimium solution. The result shows that the reducible stochastic matrix stability theorem can be applied to prove the global convergence of the algorithm. The case studys show that the algorithm has advantages of good suitability for different tvoes of OPs and hizh conver~,ence soeed when annlied to solve large-sCale ontimization r^roblems.

关 键 词：优化 蝙蝠算法 全局收敛性 进化计算 正交拉丁方 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]