ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

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 beh...

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Vydané v:Mathematics (Basel) Ročník 9; číslo 23; s. 3111
Hlavní autori: Hama Rashid, Deeam Najmadeen, Rashid, Tarik A., Mirjalili, Seyedali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.12.2021
Predmet:
ANA
Ant colony optimization
ant nesting algorithm
antenna array design
Antenna arrays
Antenna design
Comparative studies
Computer science
Design optimization
Exploitation
Fitness
Food science
Foraging behavior
Genetic algorithms
Heuristic methods
Linear programming
metaheuristic optimization algorithms
nature-inspired algorithms
Nesting
Optimization algorithms
Particle swarm optimization
Pythagorean theorem
Swarming
ISSN:2227-7390, 2227-7390
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Shrnutí: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math9233111