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

Yabao Hu, Hanning Chen, Maowei He, Liling Sun, Rui Liu and Hai Shen

Mathematical Problems in Engineering, 2019, vol. 2019, 1-22

Abstract:

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8418369

DOI: 10.1155/2019/8418369

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