@Article{EAJAM-6-3,
author = {},
title = {Direct Gravitational Search Algorithm for Global Optimisation Problems},
journal = {East Asian Journal on Applied Mathematics},
year = {2016},
volume = {6},
number = {3},
pages = {290--313},
abstract = {<p style="text-align: justify;">A gravitational search algorithm (GSA) is a meta-heuristic development that
is modelled on the Newtonian law of gravity and mass interaction. Here we propose a
new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which
combines a GSA that can perform a wide exploration and deep exploitation with the
Nelder-Mead method, as a promising direct method capable of an intensification search.
The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA
the standard GSA is run for a number of iterations before the best solution obtained is
passed to the Nelder-Mead method to refine it and avoid running iterations that provide
negligible further improvement. We test the DGSA on 7 benchmark integer functions
and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.030915.210416a},
url = {https://global-sci.com/article/82754/direct-gravitational-search-algorithm-for-global-optimisation-problems}
}