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改进的基于局部搜索策略的生物地理学优化算法
引用本文:王芙丽,李平,曹江涛.改进的基于局部搜索策略的生物地理学优化算法[J].江南大学学报(自然科学版),2012,11(4):467-473.
作者姓名:王芙丽  李平  曹江涛
作者单位:辽宁石油化工大学信息与控制工程学院,辽宁抚顺,113001
基金项目:辽宁省高校创新团队支持计划项目(LT2010058);辽宁省科技攻关项目(2011216011)
摘    要:为了提高生物地理学优化(BBO)算法的优化特性,提出一种改进的基于局部搜索策略的生物地理学优化算法(ILSBBO)。改进的算法将差分进化算法的局部搜索策略与BBO算法的迁移策略进行结合,并引入了差分进化算法中的选择操作。在13个基准测试函数上,对改进的算法、基本BBO算法,以及基于BBO的混合差分进化算法(DE/BBO)进行比较,结果表明改进的算法优于所比较的其他两种算法;此外,改进后的算法在收敛速度上也优于基本BBO算法。

关 键 词:生物地理学优化算法  差分进化算法  局部搜索策略

Improved Biogeography-Based Optimization Algorithm Based on Local Search Strategy
WANG Fu-li , LI Ping , CAO Jiang-tao.Improved Biogeography-Based Optimization Algorithm Based on Local Search Strategy[J].Journal of Southern Yangtze University:Natural Science Edition,2012,11(4):467-473.
Authors:WANG Fu-li  LI Ping  CAO Jiang-tao
Affiliation:(School of Information and Control Engineering,Liaoning Shihua University,Fushun 113001,China)
Abstract:Biogeography-Based Optimization algorithm is a new kind of global optimization algorithm,but its searching capability still needs to be improved.In order to improve the optimization characteristics of the algorithm,this paper proposes an improved algorithm of BBO——improved biogeography-based optimization algorithm based on local search strategy(ILSBBO).The improved approach combines the local search strategy of differential evolution with the migration strategy of BBO,then,the selection operation of differential evolution is introduced to it.Among 13 benchmark test functions,the improved algorithm,the original BBO,and hybrid differential evolution with biogeography-based optimization(DE/BBO) are compared,the results show that the proposed approach is better than the other two approaches.In addition,the convergence rate of the improved algorithm is also faster than the original biogeography-based optimization algorithm.
Keywords:biogeography-based optimization algorithm  differential evolution algorithm  local search strategy
本文献已被 CNKI 维普 万方数据 等数据库收录!
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