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Information Journal Paper

Title

A new algorithm for finding Pareto optimal points of multiobjective optimization problems

Pages

  141-169

Abstract

 In this paper, a modified Scalarization technique for finding Pareto optimal points of Multiobjective optimization problems is provided. The proposed method is a combination of the normal constraint and elastic constraint method. First, we introduce the optimization problem of the modified method and then we present an algorithm for obtaining the set of Pareto points. Thereafter, the relationship between optimal solutions of this Scalarization problem and (weakly, properly) efficient solutions of the Multiobjective optimization problems are analyzed. Indeed, some necessary conditions for (weak, proper) efficiency are presented. All the provided results are established without any convexity assumption. Furthermore, we propose a new algorithm for approximating the Pareto front of Multiobjective optimization problems. We solve some test problems by applying the suggested algorithm and compare the results with some existing methods, including the epsilon-constraint method, the Pascolleti-Serafini method and the NBI method. The obtained results highlight the efficiency of our approach in comparison with examined methods.

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    APA: Copy

    AKBARI, FERESHTEH, KHORRAM, ESMAILE, & Ghaznavi, Mehrdad. (2020). A new algorithm for finding Pareto optimal points of multiobjective optimization problems. MODERN RESEARCHES IN DECISION MAKING, 5(1 ), 141-169. SID. https://sid.ir/paper/397996/en

    Vancouver: Copy

    AKBARI FERESHTEH, KHORRAM ESMAILE, Ghaznavi Mehrdad. A new algorithm for finding Pareto optimal points of multiobjective optimization problems. MODERN RESEARCHES IN DECISION MAKING[Internet]. 2020;5(1 ):141-169. Available from: https://sid.ir/paper/397996/en

    IEEE: Copy

    FERESHTEH AKBARI, ESMAILE KHORRAM, and Mehrdad Ghaznavi, “A new algorithm for finding Pareto optimal points of multiobjective optimization problems,” MODERN RESEARCHES IN DECISION MAKING, vol. 5, no. 1 , pp. 141–169, 2020, [Online]. Available: https://sid.ir/paper/397996/en

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