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Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

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  • Chen, Wei

Abstract

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

Suggested Citation

  • Chen, Wei, 2015. "Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 125-139.
    • Handle: RePEc:eee:phsmap:v:429:y:2015:i:c:p:125-139
    DOI: 10.1016/j.physa.2015.02.060
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    3. Chun-Hao Chen & Jonathan Coupe & Tzung-Pei Hong, 2023. "An Accelerated Optimization Approach for Finding Diversified Industrial Group Stock Portfolios with Natural Group Detection," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
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    6. Wei Chen & Yun Wang & Mukesh Kumar Mehlawat, 2018. "A hybrid FA–SA algorithm for fuzzy portfolio selection with transaction costs," Annals of Operations Research, Springer, vol. 269(1), pages 129-147, October.
    7. Dongmei Jing & Mohsen Imeni & Seyyed Ahmad Edalatpanah & Alhanouf Alburaikan & Hamiden Abd El-Wahed Khalifa, 2023. "Optimal Selection of Stock Portfolios Using Multi-Criteria Decision-Making Methods," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
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