Open Access

ARTICLE

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Fatemeh Ahmadi Zeidabadi¹, Mohammad Dehghani², Pavel Trojovský^2,*, Štěpán Hubálovský³, Victor Leiva⁴, Gaurav Dhiman⁵

1 Department of Mathematics and Computer Sciences, Sirjan University of Technology, Sirjan, Iran
2 Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic
3 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic
4 School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, 2362807, Chile
5 Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, India

* Corresponding Author: Pavel Trojovský. Email:

(This article belongs to the Special Issue: Bio-Inspired Computational Intelligence and Optimization Techniques for Real-World Engineering Applications)

Computers, Materials & Continua 2022, 72(1), 399-416. https://doi.org/10.32604/cmc.2022.024736

Received 29 October 2021; Accepted 29 November 2021; Issue published 24 February 2022

Abstract

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

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Citations