摘要
针对标准群搜索优化算法在解决一些复杂优化问题时容易陷入局部最优且收敛速度较慢的问题,提出一种应用反向学习和差分进化的群搜索优化算法(Group Search Optimization with Opposition-based Learning and Differential Evolution,OBDGSO)。该算法利用一般动态反向学习机制产生反向种群,扩大算法的全局勘探范围;对种群中较优解个体实施差分进化的变异操作,实现在较优解附近的局部开采,以改善算法的求解精度和收敛速度。这两种策略在GSO算法中相互协同,以更好地平衡算法的全局搜索能力和局部开采能力。将OBDGSO算法和另外4种群智能算法在12个基准测试函数上进行实验,结果表明OBDGSO算法在求解精度和收敛速度上具有较显著的性能优势。
In general,the standard group search optimization algorithm（GSO）is easy to fall into the local optimum and its convergence speed is slow when solving some complex optimization problems.A group search optimization algorithm based on opposition-based leaning and differential evolution（OBDGSO）was proposed in this paper.The OBDGSO uses the opposition-based learning operator to generate the opposite population to expand the global exploration range.In addition,the operator of differential evolution（DE）is utilized to perform local exploitation to improve the solution accuracy.These two strategies are integrated into the GSO to better balance the abilities of the global convergence and local search.The OBDGSO is tested on 12 benchmark functions along with four other peering algorithms,and the experimental results show that the OBDGSO has significant performance advantages in solution accuracy and convergence speed.
作者
邹华福
谢承旺
周杨萍
王立平
ZOU Hua- fu1, XIE Cheng- wang2 ,ZHOU Yang -ping1, WANG Li -ping 3(1Information Engineering College,Jiangxi Vocational College of Industry ＆ Engineering,Pingxiang,Jiangxi 337055 ,China;2Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory,Guangxi Teachers Education University, Nanning 530023, China;3School of Information and Computer Engineering, Pingxiang University, Pingxiang, Jiangxi 337055, Chin)
出处
《计算机科学》
CSCD
北大核心
2018年第B06期124-129,共6页
Computer Science
基金
国家自然科学基金(61763010)
科学计算与智能信息处理广西高校重点实验室开放课题(GXSCIIP201604)
江西省高校人文社会科学重点基地项目(JD17127)
江西省重点研发计划项目(20071BBE50049)资助
关键词
反向学习
差分进化
群搜索优化算法
Opposition based learning
Differential evolution
Group search optimization algorithm
