A multi-external archive-guided Henry Gas Solubility Optimization algorithm for solving multi-objective optimization problems

Kahloul, Soumaia; Zouache, Djaafar; Boualem, Brahmi; Got, Adel

doi:10.1016/j.engappai.2021.104588

Cited by 14 publications

(3 citation statements)

References 30 publications

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“…Kahloul et al [128] developed the Multi-Objective Henry Gas Solubility Optimization (MOHGSO), a novel algorithm employing Pareto dominance and dual archives. MOHGSO features elite and external archives, utilizing crowding distance for leader selection and guiding the population.…”

Section: Multi-objective Hgsomentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

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Section: Multi-objective Hgsomentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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“…Accordingly, best parameters for each algorithm are used for solving the IKM of a three-section CR during tracking a spiral-like trajectory ( X = 8 t sin ( t / 2 ) , Y = 8 t cos ( t / 2 ) , 650 − 10 t ) ( t = 0 : 0 . 25 : 30 ) . The used codes to test the previously mentioned algorithm is inspired from HGSO, 38–41 GA, 42 ABC, 43 and TLBO. 44,45…”

Section: Comparison Of Pso With Four Meta-heuristic Approaches In Ter...mentioning

confidence: 99%

“…Accordingly, best parameters for each algorithm are used for solving the IKM of a threesection CR during tracking a spiral-like trajectory (X =8t sin (t=2), Y = 8t cos (t=2), 650 À 10t) (t = 0 : 0:25 : 30). The used codes to test the previously mentioned algorithm is inspired from HGSO, [38][39][40][41] GA, 42 ABC, 43 and TLBO. 44,45 Comparatively speaking, the adopted meta-heuristic approaches for solving the IKM of a three-section CR are reliably accurate since the maximum error is less than 1 3 10 À5 mm (see Table 5 and Figure 14).…”

Section: Comparison Of Pso With Four Metaheuristic Approaches In Term...mentioning

confidence: 99%

Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches

Djeffal

Mahfoudi

2023

SIMULATION

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Multi-section continuum robots’ (CRs) behavior is still an outstanding problem because of the highly non-linearity of its equation of motions. To this end, in this paper, particle swarm optimization (PSO) is adopted to solve the inverse kinematic model (IKM) of CRs. First, the CR’s structure is properly described. Then, the aforementioned algorithm is elaborately discussed and implemented in figuring out the IKM of CR and verified through forward kinematic model by choosing the PSO parameters, namely, cognitive factors [Formula: see text] and inertia weight [Formula: see text] for 200 positions on an arc-like trajectory. The optimal angle values ([Formula: see text] and [Formula: see text]) which ensure the lowest distance between the attainably desired position and the robot’s end effector are [Formula: see text] which is perfectly accurate. After that, simulation through MATLAB is carried out, namely, in the first simulation, a three-section CR follows a linear trajectory with a precision approximately equal to [Formula: see text]. Furthermore, PSO takes 7 ms as a mean consumption time to make the robot’s end effector attain to each position. Then, a circular trajectory is followed using PSO. Comparatively speaking, PSO is compared with four meta-heuristic approaches; it is remarked that PSO is a good compromise between accuracy and time consumption. Based on the obtained results, PSO can be considered as a trade-off between accuracy and time consumption for solving the IKM of CRs with complex structure.

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An Indicator Based Evolutionary Algorithm for Multiparty Multiobjective Knapsack Problems

Song,

Luo,

et al. 2024

IFIP Advances in Information and Communication Technology

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A multi-external archive-guided Henry Gas Solubility Optimization algorithm for solving multi-objective optimization problems

Cited by 14 publications

References 30 publications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches

An Indicator Based Evolutionary Algorithm for Multiparty Multiobjective Knapsack Problems

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