ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

Deeam Najmadeen Hama Rashid, Tarik A. Rashid and Seyedali Mirjalili
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Deeam Najmadeen Hama Rashid: Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil 44001, Iraq
Tarik A. Rashid: Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil 44001, Iraq
Seyedali Mirjalili: Centre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane, QLD 4006, Australia

Mathematics, 2021, vol. 9, issue 23, 1-30

Abstract: 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.

Keywords: ant nesting algorithm; ANA; metaheuristic optimization algorithms; nature-inspired algorithms; Pythagorean theorem; antenna array design; frequency-modulated synthesis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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