摘要
通过研究群体随机搜索算法解的迭代过程机制,提出区域变换搜索模型.结合反向学习(OBL)概念,提出了一般反向学习(GOBL)策略,并构造了基于一般反向学习的群体随机搜索算法的框架.理论分析证明,当父体算法收敛时,基于该算法和一般反向学习策略构造的算法也是收敛的.
By analyzing the mechanism of solutions of population-based stochastic search algorithms in iterative process, this paper presents a region transformation search model. Combining the concept of oppo- sition-based learning （OBL） ,we propose a concept of generalized opposition-based learning （GOBL） and construct a framework of population-based search algorithm with GOBL. The theoretical analyses prove that if the parent algorithm is convergent, the population-based stochastic search algorithm with general- ized opposition-based learning is also convergent.
出处
《南昌工程学院学报》
CAS
2012年第3期1-6,共6页
Journal of Nanchang Institute of Technology
基金
江西省教育厅科技项目(GJJ12641
GJJ12633)
关键词
反向学习
一般反向学习
群体随机搜索算法
演化优化
opposition-based learning
generalized opposition-based learning
population-based stochasticsearch algorithm
evolutionary optimization
