Analysis on improvement of particle memory in gravitational search algorithm

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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Multi-strategy gravitational search algorithm for constrained global optimization in coordinative operation of multiple hydropower reservoirs and solar photovoltaic power plants

Applied Soft Computing ◽

10.1016/j.asoc.2021.107315 ◽

2021 ◽

Vol 107 ◽

pp. 107315

Author(s):

Wen-jing Niu ◽

Zhong-kai Feng ◽

Shuai Liu

Keyword(s):

Global Optimization ◽

Power Plants ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Solar Photovoltaic ◽

Constrained Global Optimization ◽

Photovoltaic Power ◽

Gravitational Search ◽

Hydropower Reservoirs

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Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

Applied Sciences ◽

10.3390/app11104438 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4438

Author(s):

Satyendra Singh ◽

Manoj Fozdar ◽

Hasmat Malik ◽

Maria del Valle Fernández Moreno ◽

Fausto Pedro García Márquez

Keyword(s):

Wind Power ◽

Electricity Market ◽

Large Scale ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Optimization Methods ◽

Bidding Strategy ◽

Wind Speed Profile ◽

Gravitational Search ◽

Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

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