Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems

By R. Venkata Rao. 2016

1,836 citations recorded by the source corpus.

A simple yet powerful optimization algorithm is proposed in this paper for solving the constrained and unconstrained optimization problems. This algorithm is based on the concept that the solution obtained for a given problem should move towards the best solution and should avoid the worst solution. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. The performance of the proposed algorithm is investigated by implementing it on 24 constrained benchmark functions having different characteristics given in Congress on Evolutionary Computation (CEC 2006) and the performance is compared with that of other well-known optimization algorithms. The results have proved the better effectiveness of the proposed algorithm. Furthermore, the statistical analysis of the experimental work has been carried out by conducting the Friedman’s rank test and Holm-Sidak test. The proposed algorithm is found to secure first rank for the ‘best’ and ‘mean’ solutions in the Friedman’s rank test for all the 24 constrained benchmark problems. In addition to solving the constrained benchmark problems, the algorithm is also investigated on 30 unconstrained benchmark problems taken from the literature and the performance of the algorithm is found better.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/c1c965e93ba2c61cadebe55440f54f221eb21017

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Citations in the corpus

Coronavirus herd immunity optimizer (CHIO) (2020, neutral): “…algorithm (BA) [20], artiﬁcial bee colony (ABC) [12], sine cosine algorithm (SCA) [36], Harris hawks optimization (HHO) [33], salp swarm algorithm (SSA), and JAYA algorithm [72] are utilized to deeply investigate the efﬁciency of the proposed CHIO algorithm when compared against these algorithms.”
Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems (2020, positive): “Just like TLBO algorithm (Rao, 2015) and Jaya algorithm (Rao, 2016; Rao, 2019), these algorithms do not require any algorithm-specific parameters and thus the designer’s burden to tune the algorithm-specific parameters to get the best results is eliminated.”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, neutral): “(2015) 287 Japanese Tree Frogs Calling Algorithm (JTFCA) Hernández and Blum (2012) 288 Jaya Algorithm (JA) Rao (2016) 1 3 Table 1 (continued) SN Algorithm References 289 Kaizen Programming (KP) De Melo (2014) 290 Kernel Search Optimization (KSO) Dong and Wang (2020) 291 Keshtel Algorithm (KA)…”
Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites (2019, neutral): “tree, are optimized through Jaya algorithm [30].”
Fire Hawk Optimizer: a novel metaheuristic algorithm (2022, neutral): “…Competitive Algorithm (ICA) (Atashpaz-Gargari 1 3 and Lucas 2007), Bat Inspired Algorithm (BIA) (Yang 2010a), Sine Cosine Algorithm (SCA) (Mirjalili 2016), Jaya Algorithm (JA) (Rao 2016), Whale Optimization Algorithm (WOA) (Mirjalili and Lewis 2016), Grey Wolf Optimizer (GWO) (Mirjalili et al.”