A novel hybrid PSO-based metaheuristic for costly portfolio selection problems
提出一种混合元启发式算法，将投资组合选择问题转化为无约束问题，通过自适应更新罚参数并迭代优化，在数值案例中比恒定罚参数的粒子群优化算法表现更好，且计算时间仅为调参方法的不到4%。
Abstract In this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace -based one, but on average it just needs less than 4% of the computational time requested by the latter procedures.