Zobrazeno 1 - 10
of 2 300
pro vyhledávání: '"opposition-based learning"'
Autor:
Eirgash, Mohammad Azim, Toğan, Vedat
Publikováno v:
Engineering Computations, 2024, Vol. 41, Issue 8/9, pp. 2074-2101.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/EC-01-2024-0043
Publikováno v:
Automatika, Vol 65, Iss 4, Pp 1640-1665 (2024)
Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issu
Externí odkaz:
https://doaj.org/article/0472ef4b987d448e98be3b331061fd78
Publikováno v:
Alexandria Engineering Journal, Vol 110, Iss , Pp 77-98 (2025)
This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on
Externí odkaz:
https://doaj.org/article/db601a1467c541e189152f58b0621eae
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-24 (2024)
Abstract The sand cat swarm optimization (SCSO) is a recently proposed meta-heuristic algorithm. It inspires hunting behavior with sand cats based on hearing ability. However, in the later stage of SCSO, it is easy to fall into local optimality and c
Externí odkaz:
https://doaj.org/article/9e7e48d6783c46cead42c8c25b941295
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL
Externí odkaz:
https://doaj.org/article/1c06169466704d9db5c103aa283a2ff6
Publikováno v:
Journal of King Saud University: Engineering Sciences, Vol 36, Iss 5, Pp 330-338 (2024)
This paper explains the construction of a novel augmented hunger games search algorithm using a logarithmic spiral opposition-based learning technique. The proposed algorithm (LsOBL-HGS) is used as an efficient tool for both function optimization and
Externí odkaz:
https://doaj.org/article/8a1261988bb54f48b6518fcde17f699e
Publikováno v:
Results in Control and Optimization, Vol 17, Iss , Pp 100487- (2024)
Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. Nevertheless, BMO considers penis length to produce new offspring through pseudo
Externí odkaz:
https://doaj.org/article/c2c52dd900c24dbe8bcd16f693fb7b20
Publikováno v:
Alexandria Engineering Journal, Vol 91, Iss , Pp 348-367 (2024)
Honey badger algorithm (HBA) is a recent swarm-based metaheuristic algorithm that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity and an imbalance between exploratio
Externí odkaz:
https://doaj.org/article/53ac78084c5946bd96b8857da8e05f85
Dynamic allocation of opposition-based learning in differential evolution for multi-role individuals
Publikováno v:
Electronic Research Archive, Vol 32, Iss 4, Pp 3241-3274 (2024)
Opposition-based learning (OBL) is an optimization method widely applied to algorithms. Through analysis, it has been found that different variants of OBL demonstrate varying performance in solving different problems, which makes it crucial for multi
Externí odkaz:
https://doaj.org/article/513a5d01124b46528b257a0fffc41430
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-30 (2024)
Abstract To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstl
Externí odkaz:
https://doaj.org/article/d79bb352a58b43ff965c43389f22e304