Minimize the cost function in multiple objective optimization by using NSGA-II
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Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Verlag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © Springer Nature Switzerland AG 2019.
Açıklama
Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018 -- 27 June 2018 through 29 June 2018 -- -- 219689
Anahtar Kelimeler
Cost Function, NSGA-II Multi-Objective Problem, Vector Optimization, Vehicle Suspension System
Kaynak
Advances in Intelligent Systems and Computing
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
849
