A Multi-Strategy Adaptive Particle Swarm Optimization Algorithm for Solving Optimization Problem

In solving the portfolio optimization problem, mean-semivariance (MSV) model is more complicated and time-consuming, their relations are unbalanced because they conflict with each other due to return risk. Therefore, in order solve these existing problems, multi-strategy adaptive particle swarm optimization, namely APSO/DU, has been developed problem. present study, a constraint factor introduced control velocity weight reduce blindness search process. A dual-update (DU) strategy based on new speed, position update strategies designed. test prove effectiveness of APSO/DU algorithm, functions realistic MSV problem selected here. The results demonstrate that algorithm better convergence accuracy speed finds least risky stock for same level return. Additionally, closer global Pareto front (PF). can provide valuable advice investors good practical applications.