Henry gas solubility optimization: A novel physics-based algorithm

By Fatma A. Hashim, Essam H. Houssein, Mai S. Mabrouk, Walid Al-Atabany, S. Mirjalili. Future generations computer systems, 2019

923 citations recorded by the source corpus.

Abstract Several metaheuristic optimization algorithms have been developed to solve the real-world problems recently. This paper proposes a novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems. Henry’s law is an essential gas law relating the amount of a given gas that is dissolved to a given type and volume of liquid at a fixed temperature. The HGSO algorithm imitates the huddling behavior of gas to balance exploitation and exploration in the search space and avoid local optima. The performance of HGSO is tested on 47 benchmark functions, CEC’17 test suite, and three real-world optimization problems. The results are compared with seven well-known algorithms; the particle swarm optimization (PSO), gravitational search algorithm (GSA), cuckoo search algorithm (CS), grey wolf optimizer (GWO), whale optimization algorithm (WOA), elephant herding algorithm (EHO) and simulated annealing (SA). Additionally, to assess the pairwise statistical performance of the competitive algorithms, a Wilcoxon rank sum test is conducted. The experimental results revealed that HGSO provides competitive and superior results compared to other algorithms when solving challenging optimization problems.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ad30bee69e0b4283b1fd7b2918017f69edef937b

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Citations in the corpus

Coronavirus herd immunity optimizer (CHIO) (2020, methodology): “Table 1 Nurtured-inspired optimization algorithms Nurtured-inspired categories Nurtured-inspired algorithms Evaluation-based Genetic algorithm (GA) [9], evolution strategy (ES) [16], genetic programming (GP) [17], and biogeography-based optimizer (BBO) [18] Swarm-based Particle swarm optimization (P”
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, result): “The mathematical formulation (i.e., objective function and constraints) of the problem is detailed in Appendix A. (Gandomi, et al., 2013) HGSO (Hashim, et al., 2019) GWO (Hashim, et al., 2019) WOA (Hashim, et al The results of RUN were compared with those of MVO (Mirjalili, et al., 2016),…”
An improved grey wolf optimizer for solving engineering problems (2021, methodology): “The results of the I-GWO are compared with the state-of-the-art metaheuristic algorithms: PSO (Eberhart et al., 1995), KH (Gandomi et al., 2012), GWO (Mirjalili et al., 2014), WOA (Mirjalili et al., 2016), EEGWO (Long et al., 2018), and HGSO (Hashim et al., 2019).”
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems (2022, methodology): “Some of the popular and recent algorithms are Simulated Annealing (SA) [40], Thermal Exchange Optimization (TEO) [37], and Henry Gas Solubility Optimization [14].”