Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem

Zhao, Hong; Chen, Zong-Gan; Zhan, Zhi-Hui; Zhang, Mengjie

doi:10.1016/j.neucom.2020.12.022

Cited by 45 publications

(17 citation statements)

References 60 publications

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“…2) WFG Test Set: 6 instances, MaOEA-DDFC and NMPSO achieve the best value on 3 instances, followed by EFR-RR which obtains the best result on only 1 instance, while MOEA/D-PaS and MOMBI-II do not perform best on any instances. When comprehensively considering the best result and the second-best result, Mo4Ma-DE obtains promising performance on 14 out of 27 instances, superior to all the compared algorithms.…”

Section: B Experimental Results and Comparisonsmentioning

confidence: 99%

“…For the WFG2 test problem, when M is 5, the overall performance of Mo4Ma-DE becomes worse as k increases. When M is 10, the performance in the interval [2,6] is the best, and IGD reaches the minimum value when k = 3. 2) For some test problems, different values of k may cause a slight fluctuation of performance, such as DTLZ4 and WFG2.…”

Section: Parameter Sensitivity Analysismentioning

confidence: 99%

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A Multiobjective Framework for Many-Objective Optimization

Liu

Zhan

Tan

et al. 2022

IEEE Trans. Cybern.

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It is known that many-objective optimization problems (MaOPs) often face the difficulty of maintaining good diversity and convergence in the search process due to the highdimensional objective space. To address this issue, this article proposes a novel multiobjective framework for many-objective optimization (Mo4Ma), which transforms the many-objective space into multiobjective space. First, the many objectives are transformed into two indicative objectives of convergence and diversity. Second, a clustering-based sequential selection strategy is put forward in the transformed multiobjective space to guide the evolutionary search process. Specifically, the selection is circularly performed on the clustered subpopulations to maintain population diversity. In each round of selection, solutions with good performance in the transformed multiobjective space will be chosen to improve the overall convergence. The Mo4Ma is a generic framework that any type of evolutionary computation algorithm can incorporate compatibly. In this article, the differential evolution (DE) is adopted as the optimizer in the Mo4Ma framework, thus resulting in an Mo4Ma-DE algorithm. Experimental results show that the Mo4Ma-DE algorithm can obtain well-converged and widely distributed Pareto solutions along with the many-objective Pareto sets of the original MaOPs. Compared with seven state-of-the-art MaOP algorithms, the proposed Mo4Ma-DE algorithm shows strong competitiveness and general better performance.

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Section: B Experimental Results and Comparisonsmentioning

confidence: 99%

Section: Parameter Sensitivity Analysismentioning

confidence: 99%

A Multiobjective Framework for Many-Objective Optimization

Liu

Zhan

Tan

et al. 2022

IEEE Trans. Cybern.

Self Cite

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“…Zhou et al (2020) modeled the airline crew rostering problem as a bi-objective optimization problem that aimed at optimizing both the fairness and satisfaction of crew, and they extended the MPMO framework to efficiently solve the proposed model. Zhao et al (2021) proposed to use the MPMO framework for solving multi-objective cardinality constrained portfolio optimization problems. Liu et al (2021a) proposed to use the MPMO framework-based multiobjective PSO to solve the emergency resource dispatch problem.…”

Section: Extending Application Fieldmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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“…A hybrid complementary heuristic strategy and a local search strategy were designed in MOACS to help explore the global Pareto front sufficiently of CRP. A coevolutionary PSO algorithm was proposed by Zhao et al in [92], which was applied in solving multi-objective cardinality constrained PO problem. Bidirectional local search strategy and hybrid elite competition strategy were designed to improve the solution accuracy and bring diversity to avoid local Pareto front.…”

Section: Mpmo-based Algorithmsmentioning

confidence: 99%

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

Chen

Zhao

Yuan

2022

Mobile Information Systems

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Due to the volatility and uncertainty of the financial market, investors often use the form of portfolio to actively manage their assets. Portfolio optimization (PO) is becoming more and more important for investors. However, PO is frequently a kind of NP-hard problem in the field of modern financial optimization, which has gradually attracted the attention and interest of researchers. Some efficient mathematical models were built to describe the return and risk of portfolio. A lot of precise and approximate fast algorithms are used to solve the established PO models. The fundamental purpose is to maximize the return and to minimize the risk of portfolio under certain constraints. In recent years, researchers not only limit the goal of PO to the balance between risk and return, but also pay attention to liquidity, environmental, social, and governance (ESG) controversy level, Sortino ratio, and other indicators. The number of PO targets and constraints is further extended. In the past two decades, swarm intelligence (SI) algorithms have been widely introduced to solve PO problems. SI algorithm is mainly inspired from the daily phenomena in nature or self-organization, self-adaptation, and self-learning of biological population. The existing research results show that SI algorithm has the characteristics of high efficiency and can obtain satisfactory solutions in solving PO problems. The recent advances on the classic portfolio optimization concepts, models, and the usual SI-based solving algorithms are presented. Finally, future potential research directions are presented.

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Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem

Cited by 45 publications

References 60 publications

A Multiobjective Framework for Many-Objective Optimization

A Multiobjective Framework for Many-Objective Optimization

A survey on evolutionary computation for complex continuous optimization

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

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