改进多元宇宙算法求解大规模实值优化问题  

Application of Improved Multiverse Algorithm to Large Scale Optimization Problems

作　　者：刘小龙[1] LIU Xiaolong(School of Business Administration,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]华南理工大学工商管理学院

出　　处：《电子与信息学报》2019年第7期1666-1673,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(71471065,71571072,71771091);广州社科联基金(2018GZGJ02)~~

摘　　要：针对多元宇宙优化(MVO)算法中虫洞存在机制、白洞选择机制等不足,该文提出一种改进多元宇宙优化算法(IMVO)。设计固定概率的虫洞存在机制和前期快速收敛后期平缓收敛的虫洞旅行距离率,加快算法全局探索能力和快速迭代能力;提出黑洞的随机白洞选择机制,设计黑洞围绕白洞恒星进行公转并模型化,解决代间宇宙信息沟通的问题,中低维度数值比较实验验证了改进算法的优良性能。选取大规模实值问题较难优化的3个基准测试函数进行对比实验,改进算法在大规模优化问题上的求解精度和成功率方面具有较好的适用性和鲁棒性。To overcome the mechanism shortcomings of wormhole and white hole selection in the Multi-Verse Optimizer (MVO),an Improved Multi-Universes Optimization (IMVO) algorithm is proposed.To speed up global exploration ability and quick iteration ability,this thesis designs the existence mechanism of wormhole with fixed probability and the Travel Distance Rate (TDR) that its convergence from early stage's smoothly to later stage's fast.The random white hole selection mechanism is proposed;Black holes can revolve around selected white hole stars and is modelled to solve the problem of information communication of the Intergenerational Universes.The performance of IMVO is verified by comparison experiments in low-middle dimensions.Three benchmarks test functions are selected for comparison in large scale which are difficult to be optimized,the experimental results show that IMVO has good applicability and robustness with higher solving accuracy and success rate in large scale optimization problem.

关 键 词：大规模优化问题 多元宇宙优化 元启发式优化 非线性收敛因子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]