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Article

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

1
Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil 44001, Iraq
2
Centre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane, QLD 4006, Australia
3
Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Bo-Hao Chen
Mathematics 2021, 9(23), 3111; https://doi.org/10.3390/math9233111
Received: 20 October 2021 / Revised: 23 November 2021 / Accepted: 27 November 2021 / Published: 2 December 2021
In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems. View Full-Text
Keywords: ant nesting algorithm; ANA; metaheuristic optimization algorithms; nature-inspired algorithms; Pythagorean theorem; antenna array design; frequency-modulated synthesis ant nesting algorithm; ANA; metaheuristic optimization algorithms; nature-inspired algorithms; Pythagorean theorem; antenna array design; frequency-modulated synthesis
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MDPI and ACS Style

Hama Rashid, D.N.; Rashid, T.A.; Mirjalili, S. ANA: Ant Nesting Algorithm for Optimizing Real-World Problems. Mathematics 2021, 9, 3111. https://doi.org/10.3390/math9233111

AMA Style

Hama Rashid DN, Rashid TA, Mirjalili S. ANA: Ant Nesting Algorithm for Optimizing Real-World Problems. Mathematics. 2021; 9(23):3111. https://doi.org/10.3390/math9233111

Chicago/Turabian Style

Hama Rashid, Deeam Najmadeen, Tarik A. Rashid, and Seyedali Mirjalili. 2021. "ANA: Ant Nesting Algorithm for Optimizing Real-World Problems" Mathematics 9, no. 23: 3111. https://doi.org/10.3390/math9233111

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