双种群鱼群算法在分布式投资组合的应用  

Application of Two-population Fish Swarm Algorithm in Distributed Portfolio

作　　者：王付宇[1] 汤涛 Wang Fuyu;Tang Tao(School of Management Science and Engineering,Anhui University of Technology,Ma'anshan 243032,China)

机构地区：[1]安徽工业大学管理科学与工程学院,安徽马鞍山243032

出　　处：《系统仿真学报》2021年第9期2074-2084,共11页Journal of System Simulation

基　　金：国家自然科学基金(71872002);教育部人文社会科学青年基金(19YJCZH091);安徽省哲学社会科学规划(AHSKY2018D15);安徽普通高校重点实验室开放基金(CS2019-ZD02)。

摘　　要：针对人工鱼群算法优化精度不高和易陷入局部最优等缺点,结合引力算法和教学优化的思想,提出了一种双种群鱼群搜索算法。引入交叉思想,对双种群得出的结果进行交叉再取优,为了避免陷入局部最优,加入模拟退火的Metropoils准则。通过标准函数对算法进行验证,结果表明:双种群鱼群算法寻优效果要优于传统人工鱼群算法和已知文献算法。在已知文献基础上提出了一种分布式投资组合模型,并将设计算法应用于该进行求解,验证了该算法求解离散组合优化问题的有效性。Aiming at the disadvantages of artificial fish swarm algorithm,such as low precision and easily falling into local optimum,combining the idea of gravity algorithm and teaching optimization,a two-population fish swarm search algorithm is proposed.Cross-thinking is adopted to optimize the results obtained by the two populations and avoid the local optimization.Metropoils criterion of simulated annealing is added to the standard function verifies the algorithm,and the results show that the two-population fish swarm algorithm is better than the traditional artificial fish swarm algorithm and the known literature algorithm.Based on the known literature,a distributed portfolio model is proposed,in which the design algorithm is applied to solve the distributed portfolio model,and verifies the effectiveness of the algorithm in solving the discrete portfolio optimization problem.

关 键 词：双种群鱼群算法 引力搜索 教学优化 分布式投资组合问题 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]