关键词: 投资组合, 粒子群算法, 人工鱼群算法, 线性加权, 全局搜索

Abstract: This paper establishes a two-objective model with possibilistic mean, possibilistic variance and covariance instead of the mean value, variance and covariance in probability. The two-objective model is transformed into a single-objective model by using the linear weighting method of weighting the target, then a hybrid algorithm combined with particle swarm optimization and artificial fish swarm algorithm is designed to solve the problem. In this algorithm, the result of particle swarm optimization algorithm is taken as the initial fish group of artificial fish swarm algorithm, and the further search is made to avoid the shortcoming of particle swarm optimization. At the same time, the best position in the fish population is fed back to the velocity updating formula of the particle swarm to guide the motion of the particles and accelerate its convergence. Finally, the empirical analysis indicates that the hybrid algorithm of PSO and AFSA is effective, and the global optimum solution obtained by the hybrid algorithm is better than the one obtained by PSO.

Key words: portfolio selection;particle swarm optimization algorithm, artificial fish swarm algorithm;linear weighting;global search