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Minimize the Cost Function in Multiple Objective Optimization by Using NSGA-II

  • Hayder H. Safi
  • Tareq Abed Mohammed
  • Zena Fawzi Al-Qubbanchi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

This study proposes a new framework to minimize the cost function of multi-objective optimization problems by using NSGA-II in economic environments. For multi-objective improvements, the most generally used developmental algorithms such as NSGA-II, SPEA2 and PESA-II can be utilized. The economical optimization framework includes destinations, requirements, and parameters which continuously can change with time. The minimization of the cost function issue is one of the most important issues as in the case of stationary optimization problems. In this paper, we propose a framework that can possibly reduce the high cost of all functions that used in economic environments. Our algorithm uses a set of linear equations as inputs which depend on multi-objective algorithm that based on a Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results of our experimental study show that the proposed framework can efficiently be used to reduce the cost and time of optimizing the economical problems.

Keywords

Cost function NSGA-II multi-objective problem Vector optimization Vehicle suspension system 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hayder H. Safi
    • 1
    • 2
  • Tareq Abed Mohammed
    • 1
    • 3
  • Zena Fawzi Al-Qubbanchi
    • 4
  1. 1.Altinbas University IstanbulIstanbulTurkey
  2. 2.College of Basic EducationAl-Mustansiriya UniversityBaghdadIraq
  3. 3.College of ScienceKirkuk UniversityKirkukIraq
  4. 4.Applied ScienceUniversity of TechnologyBaghdadIraq

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