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  • Open Access


    On NSGA-II and NSGA-III in Portfolio Management

    Mahmoud Awad1, Mohamed Abouhawwash1,2,*, H. N. Agiza1

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1893-1904, 2022, DOI:10.32604/iasc.2022.023510

    Abstract To solve single and multi-objective optimization problems, evolutionary algorithms have been created. We use the non-dominated sorting genetic algorithm (NSGA-II) to find the Pareto front in a two-objective portfolio query, and its extended variant NSGA-III to find the Pareto front in a three-objective portfolio problem, in this article. Furthermore, in both portfolio problems, we quantify the Karush-Kuhn-Tucker Proximity Measure (KKTPM) for each generation to determine how far we are from the effective front and to provide knowledge about the Pareto optimal solution. In the portfolio problem, looking for the optimal set of stock or assets… More >

  • Open Access


    Hybrid Imperialist Competitive Evolutionary Algorithm for Solving Biobjective Portfolio Problem

    Chun’an Liu1,*, Qian Lei2, Huamin Jia3

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1477-1492, 2020, DOI:10.32604/iasc.2020.011853

    Abstract Portfolio optimization is an effective way to diversify investment risk and optimize asset management. Many multiobjective optimization mathematical models and metaheuristic intelligent algorithms have been proposed to solve portfolio problem under an ideal condition. This paper presents a biobjective portfolio optimization model under the assumption of no short selling. In order to obtain sufficient number of portfolio optimal solutions uniformly distributed on the portfolio efficient Pareto front, a hybrid imperialist competitive evolutionary algorithm which combines a multi-colony levy crossover operator and a simple-colony moving operator with random perturbation is also given. The performance of the More >

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