PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods

Ahrari, Ali; Elsayed, Saber M.; Sarker, Ruhul; Essam, Daryl; Coello, Carlos A. Coello

doi:10.1016/j.softx.2021.100961

Cited by 5 publications

(2 citation statements)

References 23 publications

(38 reference statements)

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“…The HGSO algorithm is assessed using the CEC2022 test suite [175], comprising twelve test functions detailed in Table 2. These functions are categorized as unimodal (F 1 ), multimodal (F 2 -F 5 ), hybrid (F 6 -F 8 ), and composition (F 9 -F 12 ) functions.…”

Section: A Benchmark Descriptionmentioning

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: A Benchmark Descriptionmentioning

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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“…It allows for a quick and simple generation of predefined problems for non-experienced users and highly customized problems for experienced users. Furthermore, it easily integrates using an arbitrary optimization method [22]. The user code can be expressed as a flow chart to show the basic elements for creating python code, as revealed in Fig.…”

Section: Computational Process By Python Codementioning

confidence: 99%

Estimation and Validation of Enrichment Percentage of U3O8 Samples using Innovative Mathematical Formula and Python Code

El-Tayebany

Elbegawy²,

El‐Mongy³

2022

JAP

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Depending on the enrichment levels, enriched uranium can be used for many purposes, such as the fabrication of fresh fuels for power and research reactors. Furthermore, under the 1968 Treaty on the Non-Proliferation (NPT), the states, specifically non-nuclear-weapon states of comprehensive safeguards agreements, should be inspected and verified to prove "peaceful use" commitments. Consequently, verifying the enrichment process and the percentage is considered an essential element for investigation purposes. The primary motivation of this paper is to estimate and validate U3O8 samples enrichment percentage using innovative formula. The enrichment calculations were conducted using certified safeguarded nuclear materials with varying low enriched uranium (LEU) ratios, both experimentally with a HpGe detector and computationally with Python 3.10 code. The results prove precise and acceptable values compared to the certified declared enrichment percentage (from depleted 0.32% - 4.5%) with differences in the range of 1⁓ 3.8%. The new formula used in this work applies to national, regional, and international safeguards inspection targets.

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Enhancing a Hierarchical Evolutionary Strategy Using the Nearest-Better Clustering

Guzowski,

Smołka,

Pekař

2024

Lecture Notes in Computer Science

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PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods

Cited by 5 publications

References 23 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

Estimation and Validation of Enrichment Percentage of U3O8 Samples using Innovative Mathematical Formula and Python Code

Enhancing a Hierarchical Evolutionary Strategy Using the Nearest-Better Clustering

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