Multi-swarm multi-objective optimizer based on \(p\)-optimality criteria for multi-objective portfolio management (Q2298863)

Summary: 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.