Details

Autor(en) / Beteiligte

• Hashim, Fatma A.
• Houssein, Essam H.
• Mabrouk, Mai S.
• Al-Atabany, Walid
• Mirjalili, Seyedali

Titel

Henry gas solubility optimization: A novel physics-based algorithm

Ist Teil von

• Future generation computer systems, 2019-12, Vol.101, p.646-667

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. •A novel physics-based metaheuristic algorithm has proposed to simulate the behavior of Henry’s law, which called HGSO.•HGSO algorithm has evaluated on several benchmarks such as 47 benchmark functions, 3 engineering design problems and CEC’17 test suite problems.•The experimental results revealed that HGSO has achieved significant superiority against the other competitive algorithms.