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Li H, Yang Y, Wang Y, Li J, Yang H, Tang J, Gao S. Population interaction network in representative gravitational search algorithms: Logistic distribution leads to worse performance. Heliyon 2024; 10:e31631. [PMID: 38828319 PMCID: PMC11140721 DOI: 10.1016/j.heliyon.2024.e31631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the particle swarm optimization algorithm (PSO), the gravitational search algorithm (GSA), and four classical variations of the gravitational search algorithm, are analyzed in terms of inter-individual information interactions and the differences in the performance of each of the algorithms on IEEE Congress on Evolutionary Computation 2017 benchmark functions. The cumulative distribution function (CDF) of the node degree obtained by the algorithm on the benchmark function is fitted to the seven distribution models by using PIN. The results show that among the seven compared algorithms, the more powerful DE is more skewed towards the Poisson distribution, and the weaker PSO, GSA, and GSA variants are more skewed towards the Logistic distribution. The more deviation from Logistic distribution GSA variants conform, the stronger their performance. From the point of view of the CDF, deviating from the Logistic distribution facilitates the improvement of the GSA. Our findings suggest that the population interaction network is a powerful tool for characterizing and comparing the performance of different MHAs in a more comprehensive and meaningful way.
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Affiliation(s)
- Haotian Li
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Yifei Yang
- Graduate School of Science and Technology, Hirosaki University, Hirosaki-shi, 036-8561, Japan
| | - Yirui Wang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Zhejiang 315211, China
- Zhejiang Key Laboratory of Mobile Network Application Technology, Zhejiang 315211, China
| | - Jiayi Li
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Haichuan Yang
- Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, 770-8506, Japan
| | - Jun Tang
- Wicresoft Co Ltd, 13810 SE Eastgate Way, Bellevue, WA 98005, USA
| | - Shangce Gao
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
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Premalatha M, Jayasudha M, Čep R, Priyadarshini J, Kalita K, Chatterjee P. A comparative evaluation of nature-inspired algorithms for feature selection problems. Heliyon 2024; 10:e23571. [PMID: 38187288 PMCID: PMC10770462 DOI: 10.1016/j.heliyon.2023.e23571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 12/02/2023] [Accepted: 12/06/2023] [Indexed: 01/09/2024] Open
Abstract
Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.
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Affiliation(s)
- Mariappan Premalatha
- School of Computer Science & Engineering, Vellore Institute of Technology, Chennai 600 127, India
| | - Murugan Jayasudha
- School of Computer Science & Engineering, Vellore Institute of Technology, Chennai 600 127, India
| | - Robert Čep
- Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic
| | - Jayaraju Priyadarshini
- School of Computer Science & Engineering, Vellore Institute of Technology, Chennai 600 127, India
| | - Kanak Kalita
- University Centre for Research & Development, Chandigarh University, Mohali, 140413, India
| | - Prasenjit Chatterjee
- Chief Research Fellow, Faculty of Civil Engineering, Institute of Sustainable Construction, Laboratory of Smart Building Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania
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Rankovic N, Rankovic D, Lukic I, Savic N, Jovanovic V. Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019. Heliyon 2023; 9:e22561. [PMID: 38034797 PMCID: PMC10687296 DOI: 10.1016/j.heliyon.2023.e22561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/02/2023] Open
Abstract
Background The presented study tracks the increase or decrease in the prevalence of seventeen different chronic non-communicable diseases in Serbia. This analysis considers factors such as region, age, and gender and is based on data from two national cross-sectional studies conducted in 2013 and 2019. The research aims to accurately identify the regions with the highest percentage of affected individuals, as well as their respective age and gender groups. The ultimate goal is to facilitate organized, free preventive screenings for these population categories within a very short time-frame in the future. Materials and methods The study analyzed two cross-sectional studies conducted between 2013 and 2019, using data obtained from the Institute of Public Health of Serbia. Both studies involved a total of 27801 participants. The study compared the performance of Decision Tree and Support Vector Regressor models with artificial neural network (ANN) models that employed two encoding functions. The new methodology for the ANN-L36 model was based on artificial neural networks constructed using a Latin square (L36) design, incorporating Taguchi's robust design optimization. Results The results of the analysis from three different models have shown that cardiovascular diseases are the most prevalent illnesses among the population in Serbia, with hypertension as the leading condition in all regions, particularly among individuals aged 64 to 75 years, and more prevalent among females. In 2019, there was a decrease in the percentage of the leading disease, hypertension, compared to 2013, with a decrease from 34.0% to 32.2%. The ANN-L36 model with Fuzzy encoding function demonstrated the highest precision, achieving the smallest relative error of 0.1%. Conclusion To date, no studies have been conducted at the national level in Serbia to comprehensively track and identify chronic diseases in the manner proposed by this study. The model presented in this research will be implemented in practice and is set to significantly contribute to the future healthcare framework in Serbia, shaping and advancing the approach towards addressing these conditions. Furthermore, experimental evidence has shown that Taguchi's optimization approach yields the best results for identifying various chronic non-communicable diseases.
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Affiliation(s)
- Nevena Rankovic
- Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, Netherlands
| | - Dragica Rankovic
- Department of Mathematics, Statistics and Informatics, Faculty of Applied Sciences, Union University “Nikola Tesla”, Dusana Popovica 22, Nis, 18000, Serbia
| | - Igor Lukic
- Department of Preventive Medicine, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, Kragujevac, 34000, Serbia
| | - Nikola Savic
- Faculty of Business Valjevo, Singidunum University, Zeleznicka 5, Valjevo, 14000, Serbia
| | - Verica Jovanovic
- Institute of the Public Health “Dr. Milan Jovanovic Batut”, dr Subotica starijeg 5, Belgrade, 11000, Serbia
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Zhong M, Wen J, Ma J, Cui H, Zhang Q, Parizi MK. A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study. Comput Biol Med 2023; 164:107212. [PMID: 37478712 DOI: 10.1016/j.compbiomed.2023.107212] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 06/18/2023] [Accepted: 06/25/2023] [Indexed: 07/23/2023]
Abstract
The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.
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Affiliation(s)
- Mingyang Zhong
- College of Artificial Intelligence, Southwest University, 400715, China.
| | - Jiahui Wen
- Defense Innovation Institute, 100085, China.
| | - Jingwei Ma
- School of Information Science and Engineering, Shandong Normal University, 250399, China.
| | - Hao Cui
- College of Artificial Intelligence, Southwest University, 400715, China.
| | - Qiuling Zhang
- College of Artificial Intelligence, Southwest University, 400715, China.
| | - Morteza Karimzadeh Parizi
- Department of Computer Engineering,Faculty of Shahid Chamran, Kerman Branch,Technical and Vocational University (TVU), Kerman, Iran.
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Bekdaş G, Cakiroglu C, Kim S, Geem ZW. Optimization and Predictive Modeling of Reinforced Concrete Circular Columns. MATERIALS (BASEL, SWITZERLAND) 2022; 15:6624. [PMID: 36233966 PMCID: PMC9573187 DOI: 10.3390/ma15196624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 09/10/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Metaheuristic optimization techniques are widely applied in the optimal design of structural members. This paper presents the application of the harmony search algorithm to the optimal dimensioning of reinforced concrete circular columns. For the objective of optimization, the total cost of steel and concrete associated with the construction process were selected. The selected variables of optimization include the diameter of the column, the total cross-sectional area of steel, the unit costs of steel and concrete used in the construction, the total length of the column, and applied axial force and the bending moment acting on the column. By using the minimum allowable dimensions as the constraints of optimization, 3125 different data samples were generated where each data sample is an optimal design configuration. Based on the generated dataset, the SHapley Additive exPlanations (SHAP) algorithm was applied in combination with ensemble learning predictive models to determine the impact of each design variable on the model predictions. The relationships between the design variables and the objective function were visualized using the design of experiments methodology. Applying state-of-the-art statistical accuracy measures such as the coefficient of determination, the predictive models were demonstrated to be highly accurate. The current study demonstrates a novel technique for generating large datasets for the development of data-driven machine learning models. This new methodology can enhance the availability of large datasets, thereby facilitating the application of high-performance machine learning predictive models for optimal structural design.
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Affiliation(s)
- Gebrail Bekdaş
- Department of Civil Engineering, Istanbul University-Cerrahpasa, 34320 Istanbul, Turkey
| | - Celal Cakiroglu
- Department of Civil Engineering, Turkish-German University, 34820 Istanbul, Turkey
| | - Sanghun Kim
- Department of Civil and Environmental Engineering, Temple University, Philadelphia, PA 19122, USA
| | - Zong Woo Geem
- Department of Smart City & Energy, Gachon University, Seongnam 13120, Korea
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