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

Shams, M F Fallah; Rashedi, Esmat; Hakimi, Ahmad

doi:10.1016/j.amc.2015.02.020

Cited by 38 publications

(20 citation statements)

References 28 publications

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“…The candidate elements in DCPC-based OM are randomly generated. However, because reducing the feasible region is an effective method to improve optimization efficiency [27][28][29][30][31][32], DCPC-based OM achieves more efficient convergence than WSGA-based HM.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In the methods of reducing feasible region, parts of the feasible region that do not include the optimum solution are deleted, and the subsequent optimization is accelerated because the remaining search space (feasible region) is smaller. Cluster analysis with the capability of dividing the feasible region into different regions has been used to reduce feasible regions [30][31][32]. Therefore, the second cluster analysis is introduced to CPC-based OM to accelerate convergence, and a novel population-based optimization method named DCPC-based OM is presented.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Dong

Wei

Tian

et al. 2017

International Journal of Aerospace Engineering

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The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Dong

Wei

Tian

et al. 2017

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

show abstract

“…Because of the advancement of the optimized heuristic algorithms and high demands of them in optimization, they become very important. These algorithms are used in many optimization problems related to the human life, such as civil engineering [1,2], electricity and telecommunications [3,4], image processing [5,6], industrial problems [7][8][9][10], filter modelling [11], medical problems [12][13][14], networking [15], economics [16], robotics [17][18][19], modern physics [20], fashion design [21], and etc.…”

Section: Introductionmentioning

confidence: 99%

“…C-GSA (Clustered-GSA), which is originated from calculating central mass of a system in nature, has been introduced to reduce complexity and computation of standard GSA. C-GSA improves the ability of GSA by reducing the number of objective function evaluations [26]. Q-GSA (Quantum GSA) has a faster convergence speed [27].…”

Section: Introductionmentioning

confidence: 99%

Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

Kherabadi

Mood

Javidi

2017

Cybernetics and Information Technologies

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Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

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“…This algorithm has been chosen due to its acceptable performance in solving various engineering optimization problems [6][7][8][9]. Based on literatures, GSA has better exploration capability [10], has better capability to escape from local optima [10], easier to implement [11] and has the ability to solve highly nonlinear optimization problems [12].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

et al. 2016

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This research is focusing on the radio frequency (RF) magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA) technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Immune System (AIS) and Ant Colony Optimization (ACO). Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

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

Cited by 38 publications

References 28 publications

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

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