A holistic review on gravitational search algorithm and its hybridization with other optimization algorithms

2017 ◽

Vol 27 (03) ◽

pp. 343-372 ◽

Cited By ~ 10

Author(s):

Umit Can ◽

Bilal Alatas

Keyword(s):

Association Rules ◽

Optimization Problems ◽

Search Algorithm ◽

Fitness Function ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Search Space ◽

Search Method ◽

Quantitative Association Rules ◽

Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2021.6.3.054 ◽

2021 ◽

Vol 6 (3) ◽

pp. 911-931

Author(s):

Abhishek Sharma ◽

Abhinav Sharma ◽

Averbukh Moshe ◽

Nikhil Raj ◽

Rupendra Kumar Pachauri

Keyword(s):

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Parameter Extraction ◽

Solar Pv ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Swarm Optimization ◽

Pv Cell ◽

Gravitational Search

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

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An adaptive gravitational search algorithm for global optimization

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i2.pp724-729 ◽

2019 ◽

Vol 16 (2) ◽

pp. 724

Author(s):

Ying-Ying Koay ◽

Jian-Ding Tan ◽

Chin-Wai Lim ◽

Siaw-Paw Koh ◽

Sieh-Kiong Tiong ◽

...

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Population Based ◽

Adaptive Search ◽

Step Size ◽

Search Mechanism ◽

Gravitational Search

<span>Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.</span>

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Parameter Extraction of Photovoltaic Module Using Tunicate Swarm Algorithm

Electronics ◽

10.3390/electronics10080878 ◽

2021 ◽

Vol 10 (8) ◽

pp. 878

Author(s):

Abhishek Sharma ◽

Ankit Dasgotra ◽

Sunil Kumar Tiwari ◽

Abhinav Sharma ◽

Vibhu Jately ◽

...

Keyword(s):

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Research Work ◽

Parameter Extraction ◽

Photovoltaic Module ◽

Solar Pv ◽

Whale Optimization ◽

Gravitational Search ◽

Swarm Algorithm

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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