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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2013 / Cilt: 21 - Sayı: 1

Orthogonal array based performance improvement in the gravitational search algorithm

Ökkeş Tolga ALTINÖZ
Asim Egemen YILMAZ
Gerhard Wilhelm WEBER
Pages
174–185
Publication Date
2013-01-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The implementation results are utilized to compare the conventional and proposed methods (i.e. conventional GSA and the so-called ``OA-GSA''), and the efficiency of the proposed method is demonstrated.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The gravitational search algorithm (GSA) is a novel heuristic method inspired by Newton's gravity and velocity equations.
📊
Key Findings & Data:Hence, the performance of the GSA to attain the optimum is related to the initial population formation, like other population-based algorithms.
💡
Conclusion & Impact:The implementation results are utilized to compare the conventional and proposed methods (i.e. conventional GSA and the so-called ``OA-GSA''), and the efficiency of the proposed method is demonstrated.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Key words: Benchmark functions · gravity based search · gravitational search algorithm

Abstract

The gravitational search algorithm (GSA) is a novel heuristic method inspired by Newton's gravity and velocity equations. In addition, it is a population-based algorithm, in which each member (called an agent) in the population has a mass, velocity, and acceleration. Beginning with the first population state, agents influence each other via mass and velocity relations. This mutual effect causes agents to reach the optimum. Hence, the performance of the GSA to attain the optimum is related to the initial population formation, like other population-based algorithms. In this study, the orthogonal array (OA) concept is applied and injected to the GSA algorithm in the initialization phase. Hence, the GSA benefits from the homogenized agent distribution tendency of the OA. The implementation results are utilized to compare the conventional and proposed methods (i.e. conventional GSA and the so-called ``OA-GSA''), and the efficiency of the proposed method is demonstrated.

Özet

The gravitational search algorithm (GSA) is a novel heuristic method inspired by Newton's gravity and velocity equations. In addition, it is a population-based algorithm, in which each member (called an agent) in the population has a mass, velocity, and acceleration. Beginning with the first population state, agents influence each other via mass and velocity relations. This mutual effect causes agents to reach the optimum. Hence, the performance of the GSA to attain the optimum is related to the initial population formation, like other population-based algorithms. In this study, the orthogonal array (OA) concept is applied and injected to the GSA algorithm in the initialization phase. Hence, the GSA benefits from the homogenized agent distribution tendency of the OA. The implementation results are utilized to compare the conventional and proposed methods (i.e. conventional GSA and the so-called ``OA-GSA''), and the efficiency of the proposed method is demonstrated.

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Cite This Article

ALTINÖZ, Ö. T., YILMAZ, A. E., WEBER, G. W. (2013). Orthogonal array based performance improvement in the gravitational search algorithm. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 21 - Sayı: 1, 174–185. https://ajindex.com/articles/188639

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