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DOI: 10.14569/IJACSA.2013.040811
PDF

On The Performance of the Gravitational Search Algorithm

Author 1: Taisir Eldos
Author 2: Rose Al Qasim

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 8, 2013.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Gravitational Search Algorithms (GSA) are heuristic optimization evolutionary algorithms based on Newton's law of universal gravitation and mass interactions. GSAs are among the most recently introduced techniques that are not yet heavily explored. An early work of the authors has successfully adapted this technique to the cell placement problem, and shown its efficiency in producing high quality solutions in reasonable time. We extend this work by fine tuning the algorithm parameters and transition functions towards better balance between exploration and exploitation. To assess its performance and robustness, we compare it with that of Genetic Algorithms (GA), using the standard cell placement problem as benchmark to evaluate the solution quality, and a set of artificial instances to evaluate the capability and possibility of finding an optimal solution. Experimental results show that the proposed approach is competitive in terms of success rate or likelihood of optimality and solution quality. And despite that it is computationally more expensive due to its hefty mathematical evaluations, it is more fruitful on the long run.

Keywords: Optimization; Gravitational Search; Genetic Algorithms; Cell Placement

Taisir Eldos and Rose Al Qasim, “On The Performance of the Gravitational Search Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 4(8), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040811

@article{Eldos2013,
title = {On The Performance of the Gravitational Search Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040811},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040811},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {8},
author = {Taisir Eldos and Rose Al Qasim}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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