摘要
提出了一种基于双模式迁移策略的生物地理学优化算法。该算法在标准生物地理学优化算法的基础上,引入自适应的差分变异算子对迁移算子进行改善,并将改进后迁移算子与标准的迁移算子相结合形成双迁移模式,同时,通过调节参数对两种迁移模式加以平衡。利用10个基准测试函数进行测试,结果表明,与两种单模式算法相比,改进后的生物地理学优化算法优化性能提升,其收敛速度、收敛精度提高、算法稳定性具有明显优势,且当参数取0.65～0.75时算法的性能达到最优。
An improved biogeography-based optimization (BBO) algorithm based on the dual-mode migration strategy is proposed. Based on the standard BBO algorithm, an adaptive differentia l mutation operator is introduced to improve the migration operator, and the improved migration operator is combined with the standard migration operator to form a dual-mode migration strategy. Meanwhile, the two migration modes are balanced by adjusting the parameters . The algorithm is applied to 10 benchmark functions to test the performance. Results show that compared with two single-mode algorithms, the performance of the improved BBO is increased, and BBO has distinct superiority in terms of convergence speed, convergence precision and algorithm stability .When the parameters are taken from 0.65 to 0.75, the performance of the algorithm is optimal.
作者
李昌兴
张颖
LI Changxing;ZHANG Ying(School of Science, Xi'an University of Posts and Telecommunications, Xi'an 710121, China)
出处
《西安邮电大学学报》
2019年第1期73-78,84,共7页
Journal of Xi’an University of Posts and Telecommunications
基金
陕西省自然科学基金资助项目(2014JM8307)
关键词
生物地理学优化算法
双模式迁移策略
差分演化
biogeography-based optimization algorithm
dual-mode migration strategy
differential evolution
