摘要
针对群搜索优化(Group Search Optimizer,GSO)算法易陷入局部最优、收敛速度较慢、收敛精度较低等问题,提出一种基于差分策略的群搜索优化(Differential Ranking-based Group Search Optimizer,DRGSO)算法。主要进行两方面改进:1)按照适应度值的大小对种群进行排序,适当增加发现者的数目,使种群能够获得更好的启发式信息,加快了算法的收敛速度,有效地避免了算法陷入局部最优;2)在发现者搜索过程中,引入4种不同的差分变异策略,提高了算法的收敛精度,增强了算法的群体多样性在。11组国际标准测试函数上的实验测试结果显示,与GA,GSO,PSO算法相比,DRGSO算法具有较强的全局搜索能力以及局部资源勘探能力,算法整体收敛性能明显提高。
The conventional group search optimizer(GSO)is not free from some drawbacks such as easily falling into local optimum,a relative long computing time and lower convergence accuracy.In this study,we proposed a differential ranking-based group search optimizer(DRGSO)algorithm to alleviate these limitations.There are mainly two improvements in the design of DRGSO.First,the population is initialized according to the ranking of fitness values.With this regard,the population obtains heuristic information and alleviates premature convergence to some extent.Second,four evolutionary operators based on differential strategies are constructed to improve the convergence of the algorithm and enhance the population diversity.To demonstrate the performance,eleven benchmark functions were included to evaluate the performance of DRGSO.Experimental results indicate that the proposed DRGSO exhibits better performance in comparison with the GA,PSO and GSO in terms of accuracy and speed of convergence.
作者
熊聪聪
郝璐萌
王丹
邓雪晨
XIONG Cong-cong HAO Lu-meng WANG Dan DENG Xue-chen(College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, Chin)
出处
《计算机科学》
CSCD
北大核心
2017年第2期250-256,共7页
Computer Science
基金
天津市高等学校科技发展基金计划项目(20140803)
天津科技大学青年教师创新基金(2014CXLG30)
国家自然基金面上项目(61272509)
国家自然科学基金青年基金(61402332)资助
关键词
群搜索优化算法
差分变异
收敛速度
收敛精度
Group search optimizer(GSO)
Differential mutation
Convergence speed
Convergence accuracy