Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management

Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.