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Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

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  • Shams, Masumeh
  • Rashedi, Esmat
  • Hakimi, Ahmad

Abstract

Gravitational search algorithm (GSA) is a recent introduced algorithm which is inspired by law of gravity and mass interactions. In this paper, a novel version of GSA, named Clustered-GSA, is proposed to reduce complexity and computation of the standard GSA. This algorithm is originated from calculating central mass of a system in nature and improves the ability of GSA by reducing the number of objective function evaluations. Clustered-GSA is evaluated on two sets of standard benchmark functions and the results are compared with several heuristic algorithms and a deterministic optimization algorithm. Experimental results show that by using Clustered-GSA, better results are achieved with lower complexity. Moreover, the proposed algorithm is used to optimize the parameters of a Low Noise Amplifier (LNA) in order to achieve the required specifications. LNA is the first stage in a receiver after the antenna. The main performance characteristics of receivers are dictated by the LNA performance. It is necessary to study, design, and optimize all the elements included in the structure, simultaneously. The comparative results show the efficiency of the proposed algorithm.

Suggested Citation

  • Shams, Masumeh & Rashedi, Esmat & Hakimi, Ahmad, 2015. "Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 436-453.
    • Handle: RePEc:eee:apmaco:v:258:y:2015:i:c:p:436-453
    DOI: 10.1016/j.amc.2015.02.020
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    Cited by:

    1. Ren, Ting & Sun, Yang & Zhang, Jiye & Yan, Gaocheng & Mu, Huaiping & Liu, Shi, 2016. "Optimal energy use of the collector tube in solar power tower plant," Renewable Energy, Elsevier, vol. 93(C), pages 525-535.
    2. Hedieh Sajedi & Seyedeh Fatemeh Razavi, 2017. "DGSA: discrete gravitational search algorithm for solving knapsack problem," Operational Research, Springer, vol. 17(2), pages 563-591, July.
    3. Amirhossein Tahmouresi & Esmat Rashedi & Mohammad Mehdi Yaghoobi & Masoud Rezaei, 2022. "Gene selection using pyramid gravitational search algorithm," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-15, March.

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