Quantum Henry gas solubility optimization algorithm for global optimization

This paper proposes an improvement on the recently introduced Henry Gas Solubility Optimization (HGSO) metaheuristic algorithm that simulates Henry’s gas law (i.e., the concentration of a gas sample in a liquid solvent is proportional to the concentration of the sample in the gas phase). As an impro...

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Bibliographic Details
Published in:Engineering with computers Vol. 38; no. Suppl 3; pp. 2329 - 2348
Main Authors: Mohammadi, Davood, Abd Elaziz, Mohamed, Moghdani, Reza, Demir, Emrah, Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: London Springer London 01.08.2022
Springer Nature B.V
Subjects:
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Gas sampling
Gas solubility
Global optimization
Heuristic methods
Math. Applications in Chemistry
Mathematical and Computational Engineering
Optimization
Original Article
Quantum theory
Solution space
Systems Theory
Vapor phases
Physics-based algorithms
Henry gas solubility optimization algorithm
Metaheuristics
Quantum theory
Optimization
ISSN:0177-0667, 1435-5663
Online Access:Get full text
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Summary:This paper proposes an improvement on the recently introduced Henry Gas Solubility Optimization (HGSO) metaheuristic algorithm that simulates Henry’s gas law (i.e., the concentration of a gas sample in a liquid solvent is proportional to the concentration of the sample in the gas phase). As an improvement, we apply quantum theory instead of the standard procedure used in the HSGO algorithm for updating solutions. The proposed algorithm is named as Quantum HGSO (QHGSO) algorithm in this paper. The suggested changes enhance the ability of HGSO to create a counterbalance between exploitation and exploration for a better investigation of the solution space. For evaluating the capability of finding the optimal solution of our proposed algorithm, a collection of forty-seven global optimization functions is solved. Moreover, three well-known engineering problems are studied to show the performance of the QHGSO algorithm in constrained optimization problems. Comparative results with other well-known metaheuristic algorithms have shown that the QHGSO algorithm outperforms others with higher computational performance.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-021-01347-1