International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 15, No. 2, 8 Apr. 2023
Cover page and Table of Contents: PDF (size: 442KB)
Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence
Full Text (PDF, 442KB), PP.62-72
Views: 0 Downloads: 0
Author(s)
Vasyl Lytvyn
1
Olga Lozynska
1
Dmytro Uhryn
2
Myroslava Vovk
1,*
Yuriy Ushenko
2
Zhengbing Hu
3
Index Terms
Industry Geographic Information System, Swarm Algorithm, Decision Support System, Objective Function, Optimization Methods.
Abstract
A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods).
Cite This Paper
Vasyl Lytvyn, Olga Lozynska, Dmytro Uhryn, Myroslava Vovk, Yuriy Ushenko, Zhengbing Hu, "Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.2, pp. 62-72, 2023. DOI:10.5815/ijmecs.2023.02.06
Reference
[1]Meyerhenke H. Shape Optimizing Load Balancing for MPI-Parallel Adaptive Numerical Simulations / H. Meyerhenke // 10th DIMACS Implementation Challenge on Graph Partitioning and Graph Clustering, – 2013. – P. 67–82.
[2]Pham D. Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms / D. Pham, M. Castellani // Soft Computing. – 2013. – Vol. 18, 5. – P. 1-33.
[3]Esmin, Ahmed A. A., & Matwin, Stan. (2012). Data clustering using hybrid particle swarm optimization. In Hujun Yin, José A. F. Costa, & Guilherme Barreto (Eds.), Intelligent data engineering and automated learning - IDEAL 2012 (pp. 159–166). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_20.
[4]Peleshko D. Image Superresolution via Divergence Matrix and Automatic Detection of Crossover / D. Peleshko, T. Rak, I. Izonin // International Journal of Intelligent Systems and Applications. № 8(12). – 2016. P. 1–8.
[5]Roman Bihun, Vasyl Lytvyn. Optimization of garbage removal within a territorial community // Eastern-European Journal of Enterprise Technologies, 2022. - № 1 (3) – Р. 115-125.
[6]Parsopoulos K. E. Multi-Objective Particles Swarm Optimization Approaches / K. E. Parsopoulos, M. N. Vrahatis // Multi-Objective Optimization in Computational Intelligence. – IGI Global, 2008. – P. 20–42. doi:10.4018/978-1-59904-498-9.ch002.
[7]Lytvyn V. Development of the method for territorial community formation based on multicriteria swarm algorithm approach / V. Lytvyn, D. Uhryn, S. Shevchuk, O. Baliasnikova, O. Iliiyuk // Information and control systems Information technologies: Technology audit and production reserves. – 2017. –№ 3/2 (35). – P. 20-27.
[8]Lytvyn V. Modeling of the process of territorial communities formation using swarm intelligence algorithms / V. Lytvyn, D. Uhryn, N. Nadiein, O. Klichuk // Information and control systems Information technologies: Technology audit and production reserves. – 2017. – № 5/2 (37). – P. 17-33.
[9]Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution - an updated survey. Swarm and Evolutionary Computation, 27, 1–30. https://doi.org/10.1016/j.swevo.2016.01.004.
[10]Zhan Zhi-hui Adaptive Particle Swarm Optimization / Zhi-hui Zhan, Jun Zhang // Systems, Man, and Cybernetics, Part B: Cybernetics. – December 2009. – Vol. 39. – № 6. – P. 1362 – 1381.
[11]Yang X.-S. Efficiency Analysis of Swarm Intelligence and Randomization Techniques / X.-S. Yang // Journal of Computational and Theoretical Nanoscience. – 2012. – Vol. 9, № 2. – P. 189–198. doi:10.1166/jctn.2012.2012.
[12]Qasem, M., Ying, Y., Wang, Z., Thulasiraman, P., & Thulasiram, R. (2018). Enhancing ant brood clustering with adaptive radius of perception and non-parametric estimation on multi-core architectures. In Advances in intelligent networking and collaborative systems. INCoS 2017. Springer.
[13]Wang X. Improved multi-objective ant colony optimization algorithm and its application in complex reasoning / X. Wang, Y. Zhao, D. Wang, H. Zhu, Q. Zhang // Chinese Journal of Mechanical Engineering. – 2013. – Vol. 26, № 5. – P. 1031–1040. doi:10.3901/cjme.2013.05.1031.
[14]Yang X. Bat algorithm: literature review and applications. / X. Yang // International Journal of Bio-Inspired Computation Vol. 5. – № 3, 2013. – Р. 141–149.
[15]Tvrdik, J., & Křivỳ, I. (2015). Hybrid differential evolution algorithm for optimal clustering. Applied Soft Computing, 35, 502–512.
[16]Maithri. C., Chandramouli H., "Parallel DBSCAN Clustering Algorithm Using Hadoop Map-reduce Framework for Spatial Data", International Journal of Information Technology and Computer Science, Vol.14, No.6, pp.1-12, 2022.
[17]Selim Y. New Modification Approach on Bat Algorithm for Solving Optimization Problems / Y. Selim, U. Ecir // Applied Soft Computing, – 2014. – P. 1–16.
[18]Nakamura R. M BBA: a binary bat algorithm for feature selection / R. Nakamura, L. Pereira, K. Costa and other // Сonference on graphics, patterns and images (25th SIBGRAPI), August 22–25, – 2012: IEEE Publication. – Р. 291–297. DOI:10.1109/ SIBGRAPI.2012.47.
[19]Kong M. Application of ACO in Continuous Domain / M. Kong, P. Tian, L. Jiao et al. (Eds.): ICNC 2006, LNCS 4222. – Part II. – Berlin: Springer-Verlag. – 2006. – P. 126–135.
[20]Pham D. The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems / D. Pham, M. Castellani // Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. – 2009. – Vol. 223. – C. 2919-2938.
[21]Aleti, A., & Moser, I. (2016). A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Computing Survey, 49(3), 56–15635. https://doi.org/10.1145/2996355.
[22]Maithri. C., Chandramouli H., "Parallel DBSCAN Clustering Algorithm Using Hadoop Map-reduce Framework for Spatial Data", International Journal of Information Technology and Computer Science, Vol.14, No.6, pp.1-12, 2022.
[23]Adelia Juli Kardika, Aulia Khoirunnita, Salman, Saharuddin, Indah Muliana, "Development Web-GIS of Commodity Information System for Agriculture, Establishment and Forestry in Marangkayu District", International Journal of Education and Management Engineering, Vol.12, No.5, pp. 1-8, 2022.
[24]Atam Kumar, Hafiz Karim Bux Indher, Ali Gul, Rab Nawaz, "Analysis of Risk Factors for Work-related Musculoskeletal Disorders: A Survey Research", International Journal of Engineering and Manufacturing, Vol.12, No.6, pp. 1-13, 2022.
[25]Zhengbing Hu, Yulia Khokhlachova, Viktoriia Sydorenko, Ivan Opirskyy, "Method for Optimization of Information Security Systems Behavior under Conditions of Influences", International Journal of Intelligent Systems and Applications, Vol.9, No.12, pp.46-58, 2017.