计算机工程与应用2012，Vol.48Issue(34)：140-143,4.DOI:10.3778/j.issn.1002-8331.1105-0207
多目标优化中带变异算子的灰色粒子群算法
Grey particle swarm algorithm with mutation operator in multi-objective optimization
摘要
Abstract
A particle swarm algorithm based on mutation operator is put forward for getting better Pareto solution sets in the field of multi-objective optimization problem. The grey correlation degree theory, which is introduced by variance, is applied to the algorithm for distinguishing the sequences with equal means but sensible difference of object position correlation coefficients. The particle swarm algorithm is controlled by this mutation strategy. Hence, the local convergence phenomenon is prevented during solving multi-objective problem with grey particle swarm algorithm. The algorithm's performance is tested by four groups of different types of benchmark functions. It shows that the algorithm can not only convergence to the Pareto optimal solution sets very well but also efficiently avoid falling into the local optimal solution too early.关键词
多目标/变异算子/灰色关联度/粒子群算法/优化Key words
multi-objective/ mutation operator/ grey relational degree/ Particle Swarm algorithm/ optimization分类
信息技术与安全科学引用本文复制引用
彭耶萍,董坚峰..多目标优化中带变异算子的灰色粒子群算法[J].计算机工程与应用,2012,48(34):140-143,4.基金项目
国家自然科学基金项目(No.71073121). （No.71073121）
