A novel hybrid PSO-based metaheuristic for costly portfolio selection problems

Abstract In this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which tailor portfolio selection problem. To motivate and apply our metaheuristic, reformulate the problem as an unconstrained problem, by means of penalty functions in framework exact methods. Our is it adaptively updates parameters model during optimization process. addition, iteratively refines its solutions to reduce possible infeasibilities. We report also numerical case study. appears perform better than corresponding Optimization solver with constant parameters. It performs similarly two solvers respectively determined REVAC-based tuning procedure irace -based one, but average just needs less 4% computational time requested latter procedures.