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

Masumeh Shams, Esmat Rashedi and Ahmad Hakimi

Applied Mathematics and Computation, 2015, vol. 258, issue C, 436-453

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.

Keywords: Optimization; Gravitational search algorithm; Clustered-GSA; Multi-objective problems; Low noise amplifier (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:258:y:2015:i:c:p:436-453

DOI: 10.1016/j.amc.2015.02.020

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