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Open AccessArticle

Fine-Tuning Meta-Heuristic Algorithm for Global Optimization

1
College of Engineering, Department of Computer Engineering, University of Baghdad, Al-Jadriyah, Baghdad 10001, Iraq
2
College of Engineering, Department of Electrical Engineering, University of Baghdad, Al-Jadriyah, Baghdad 10001, Iraq
3
Department of Control and Systems Engineering, University of Technology, Baghdad 10001, Iraq
*
Author to whom correspondence should be addressed.
Processes 2019, 7(10), 657; https://doi.org/10.3390/pr7100657
Received: 20 August 2019 / Revised: 5 September 2019 / Accepted: 10 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Optimization for Control, Observation and Safety)
This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown to check the performance of each algorithm, and the other test for 30 trials to measure the statistical results of the performance of the proposed algorithm against the others. Results confirm that the proposed FTMA global optimization algorithm has a competing performance in comparison with its counterparts in terms of speed and evading the local minima. View Full-Text
Keywords: global optimization; meta-heuristics; swarm intelligence; benchmark functions; exploration; exploitation; global minimum; local minimum global optimization; meta-heuristics; swarm intelligence; benchmark functions; exploration; exploitation; global minimum; local minimum
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Allawi, Z.T.; Ibraheem, I.K.; Humaidi, A.J. Fine-Tuning Meta-Heuristic Algorithm for Global Optimization. Processes 2019, 7, 657.

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