Improved Particle Swarm Optimizer and Its Application for CvaR Model
Yanmin Liu, Zhao Qing-zhen · Shuxue de shijian yu renshi · 2011
In order to solve the mean-variance portfolio model with conditional value-at-risk (CVaR) constraint,a PSO algorithm based on comprehensive learning and Cauchy mutation is proposed.In CCPSO,to improve the ability to escape from local optima,a comprehensive learning strategy is adopted,which increase the probability of flying to the optimal solution. And a dynamic mutation is introduced to make the Cauchy mutation for each pbest.At last,in terms of the condition of the best performing particle(gbest) in the swarm,at each iteration,the mutation operation is employed to generate the new gbest.The experiments on benchmarks indicate that the proposed algorithm has good performance.In the CvaR model,the CCPSO algorithm is feasible and effective,and better results compared with other algorithms.