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Refining COVID-19 Lesion Segmentation in Lung CT Scans Using swarm intelligence and Evolutionary algorithms

INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY 2025年第1期21卷 e70044页

作者： Gtifa, Wafa Fradi, Marwa Sakly, Anis Machhout, Mohsen Univ Monastir Monastir Natl Sch Engineers ENIM Lab Automat & Elect Syst & Environm Monastir Tunisia Univ Monastir Fac Sci Monastir Lab Elect & Microelect Monastir Tunisia

BackgroundAccurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID-19) pandemic. swarm intelligence algorithms offer promising tools for this *** study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle swarm Optimization (PSO) for segmenting COVID-19 lung ***, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation *** findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.

关键词： BFOA computed tomography COVID-19 GA GSA PSO segmentation swarm intelligence algorithms

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swarm intelligence algorithms for Circles Packing Problem with equilibrium Constraints

Swarm intelligence algorithms for Circles Packing Problem wi...

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12th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Wang, Peng Huang, Shuai Zhu, ZhouQuan Northwestern Polytech Univ Coll Marine Xian 710072 Peoples R China

ISBN: (纸本)9780769550602

Circles packing problem with equilibrium constraints is difficult to solve due to its NP-hard nature. Aiming at this NP-hard problem, three swarm intelligence algorithms are employed to solve this problem. Particle swarm Optimization and Ant Colony Optimization has been used for the circular packing problem with equilibrium constraints. In this paper, Artificial Bee Colony Algorithm (ABC) for equilibrium constraints circular packing problem is presented. Then we compare the performances of well-known swarm intelligence algorithms (PSO, ACO, ABC) for this problem. The results of experiment show that ABC is comparatively satisfying because of its stability and applicability.

关键词： Circles Packing swarm intelligence algorithms Equilibrium Constraints

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A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第2期32卷 567-588页

作者： Phan, Han Duy Ellis, Kirsten Barca, Jan Carlo Dorin, Alan Monash Univ Fac IT Clayton Vic 3800 Australia Deakin Univ Fac Sci Engn & Built Environm Sch IT Burwood 3125 Australia

Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.

关键词： swarm intelligence algorithms Dynamic parameter setting Parameter control

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A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

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swarm AND EVOLUTIONARY COMPUTATION 2011年第1期1卷 3-18页

作者： Derrac, Joaquin Garcia, Salvador Molina, Daniel Herrera, Francisco Univ Granada CITIC UGR Res Ctr Informat & Commun Technol Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Cadiz Dept Comp Engn Cadiz 11003 Spain

The interest in non parametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Statistical analysis Nonparametric statistics Pairwise comparisons Multiple comparisons Evolutionary algorithms swarm intelligence algorithms

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Structural damage identification with output-only strain measurements and swarm intelligence algorithms: a comparative study

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第5期35卷 056125-056125页

作者： Zhang, Guangcai Hou, Jiale Feng, Kun Wan, Chunfeng Xie, Liyu Xue, Songtao Noori, Mohammad Ding, Zhenghao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Tsinghua Univ Dept Civil Engn Beijing Peoples R China Queens Univ Belfast Sch Nat & Built Environm Belfast North Ireland Queens Univ Belfast Sch Mech & Aerosp Engn Belfast North Ireland Tongji Univ Dept Disaster Mitigat Struct Shanghai Peoples R China Tohoku Inst Technol Dept Architecture Sendai Japan Calif Polytech State Univ Dept Polit Arcata CA USA Univ Leeds Sch Civil Engn Leeds England Kyoto Univ Kyoto Japan

The identification of structural damage with the unavailability of input excitations is highly desired but challenging since structural dynamic responses are affected by the coupling effect of structural parameters and external excitations. To deal with this issue, in this paper, an output-only damage identification strategy based on swarm intelligence algorithms and correlation functions of strain responses is proposed to identify structures subjected to single or multiple unknown white noise excitations. In the proposed strategy, four different population-based optimization algorithms-particle swarm optimization, the butterfly optimization algorithm, the tree seed algorithm, and a micro search Jaya (MS-Jaya)-are employed and compared. The micro search mechanism is integrated into a basic Jaya algorithm to improve its computational efficiency and accuracy by eliminating some damage variables with small values for the identified best solution after several iterations. The objective function is established based on the proposed auto/cross-correlation function of strain responses and a penalty function. The effectiveness of the proposed method is verified with numerical studies on a simply supported beam structure and a steel grid benchmark structure under ambient excitation. In addition, the effect of the reference point, number of sensors, and arrangement of strain gauges on the performance of the proposed method are discussed in detail. The investigated results demonstrate that the proposed approach can accurately detect, locate, and quantify structural damage with limited sensors and 20% noise-polluted strain responses. In particular, the proposed MS-Jaya algorithm presents a more superior capacity in solving the optimization-based damage identification problem than the other three algorithms.

关键词： damage identification swarm intelligence algorithms Jaya algorithm strain responses correlation function micro search mechanism

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Multi-objective scheduling of relief logistics based on swarm intelligence algorithms and spatio-temporal traffic flow

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Journal of Safety Science and Resilience 2021年第4期2卷 222-229页

作者： Zhiming Ding Zilin Zhao Detian Liu Yang Cao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Institute of Software Chinese Academy of ScienceBeijing 100190China School of Information Beijing Wuzi UniversityBeijing 101149China

Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient *** rescue time,scheduling cost and demanders’satisfac-tion as goals,in this paper,an emergency supplies scheduling model based on multi-objective optimization was proposed to provide a wealth of decision-making *** four multi-objective optimization algorithms are employed to obtain the optimal set of scheduling *** addition,we design the minimum time cost model and the shortest route cost model by considering the change of the road network *** extensive simulation experiments are conducted on a real urban traffic *** experimental results show that the two cost models can serve different scheduling needs and provide efficient scheduling for emergency supplies.

关键词： Logistics scheduling MOP swarm intelligence algorithms Spatio-temporal trajectory Traffic flow

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Financial sequence prediction based on swarm intelligence algorithms and internet of things

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JOURNAL OF SUPERCOMPUTING 2022年第15期78卷 17470-17490页

作者： Gao, Zheng Zhang, Chenxiang Li, Zhengyin Shandong Agr Univ Sch Econ & Management Tai An 271000 Shandong Peoples R China Univ Illinois Dept Ind & Enterprise Syst Engn Champaign IL 61820 USA Chinese Acad Agr Sci Inst Agr Econ & Dev Beijing 100081 Peoples R China

This paper aims to predict the financial time series. swarm intelligence algorithms usually use metadata to ensure objectivity without the statistical assumptions of the data. This paper proposed a prediction algorithm integrating multiple support vector regression (SVR) models. The algorithm selects different datasets to train these SVR models. This algorithm also adopts reasonable weights to combine the forecasting results of multiple models to reduce the overall prediction error. The weight of each model is dynamically adjusted according to its recent prediction accuracy. Therefore, this algorithm is adaptive and can deal with nonstationary problems. Five international authoritative stock indexes are used to compare the hybrid SVR model with a single SVR model for performance validation from the perspectives of normalized mean squared error, weighted directional symmetry, and root mean squared error. The results demonstrate that the hybrid SVR model has significantly improved the prediction accuracy and generalization ability of the prediction algorithm compared with a single SVR model. It reveals that selecting the appropriate input vector can achieve an excellent prediction effect.

关键词： Financial time series swarm intelligence algorithms Stock prediction Fuzzy theory

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Parallelization of swarm intelligence algorithms: Literature Review

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2022年第5-6期50卷 486-514页

作者： Menezes, Breno Augusto de Melo Kuchen, Herbert de Lima Neto, Fernando Buarque Univ Munster Dept Informat Syst Leonardo Campus 3 D-48149 Munster NRW Germany Univ Pernambuco ECOMP Rua Benf 455 BR-50720001 Recife PE Brazil

swarm intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

关键词： Metaheuristics swarm intelligence algorithms Parallel computing Cluster computing High-performance computing

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swarm intelligence algorithms in Text Document Clustering with Various Benchmarks

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SENSORS 2021年第9期21卷 3196-3196页

作者： Selvaraj, Suganya Choi, Eunmi Kookmin Univ Dept Financial Informat Secur Seoul 02707 South Korea Kookmin Univ Dept Software Coll Comp Sci Seoul 02707 South Korea

Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

关键词： swarm intelligence algorithms text document clustering artificial intelligence data mining

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Optimal Estimation of Schottky Diode Parameters Using Advanced swarm intelligence algorithms

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SEMICONDUCTORS 2020年第11期54卷 1398-1405页

作者： Rabehi, A. Nail, B. Helal, H. Douara, A. Ziane, A. Amrani, M. Akkal, B. Benamara, Z. Univ Djillali Liabes Sidi Bel Abbes Lab Microelect Appl BP 89 Sidi Bel Abbes 22000 Algeria Tissemsilt Univ Ctr Inst Sci & Technol Tissemsilt 38000 Algeria

This work deals with estimation of the Schottky diode (Au|GaN|GaAs) optimal parameters. For this purpose, advanced swarm intelligence (SI) algorithms have been applied, i.e., Harris hawks optimization, ant lion optimizer (ALO), grey wolf optimizer, and whale optimization algorithm. The performance of the SI algorithms has been investigated by a comparative study following the analytical methods developed by Kaminski I, Cheung and Cheung, Norde, and Mikhelashvili. The comparative results show that the ALO algorithm gives minimum RMSE criteria, with best parameters estimation against all the SI optimizers and the analytical techniques.

关键词： Schottky diodes Au GaN GaAs swarm intelligence algorithms parameters estimation barrier height

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