Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management. (21st January 2019)

• Record Type: Journal Article
• Authors: Hu, Yabao; Chen, Hanning; He, Maowei; Sun, Liling; Liu, Rui; Shen, Hai
• Other Names: Perera Ricardo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-01-21
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/8418369
• Languages: English
• ISSNs: 1024-123X

Abstract

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.