Vol.42, No.2, 2022, pp.727-737, doi:10.32604/csse.2022.023018
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ARTICLE
Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm
  • Mashar Gencal1,*, Mustafa Oral2
1 Ardahan University, Ardahan, 75003, Turkey
2 Cukurova University, Adana, 01330, Turkey
* Corresponding Author: Mashar Gencal. Email:
(This article belongs to this Special Issue: Modelling, Simulation and Optimization of Complex Systems Using Computational Intelligence)
Received 25 August 2021; Accepted 27 September 2021; Issue published 04 January 2022
Abstract
Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males. In the mating of these polygamous creatures, there is competition between males as well as among their offspring. Thus, male reproductive success depends on both male competition and sperm rivalry. Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed. The algorithm was modelled and implemented based on the sexual behavior of roosters. 13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of RA with the performance of well-known algorithms; Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Grey Wolf Optimizer (GWO). Also, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank Tests, were performed to demonstrate the significance of the results. In 20 of the 23 functions that were tested, RA either offered the best results or offered similar results to other compared algorithms. Thus, in this paper, we not only present a novel nature-inspired algorithm, but also offer an alternative method to the well-known algorithms commonly used in the literature, at least as effective as them.
Keywords
Evolutionary computation; meta-heuristics; optimization; roosters algorithm
Cite This Article
Gencal, M., Oral, M. (2022). Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm. Computer Systems Science and Engineering, 42(2), 727–737.
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