Review on swarm intelligent techniques and their applications in different area
DOI:
https://doi.org/10.29304/jqcm.2022.14.2.935Keywords:
Swarm Intelligent, Metaheuristics, Optimization, Genetic Algorithm, Ant Colony, Bee Colony, Whale optimization algorithm, Salp Swarm, Particle Swarm Optimization, Tunicate Swarm, Bird swarm, Cat swarm optimizationAbstract
Broadly, swarm intelligence (SI) algorithms are considered as nature-inspired techniques improved depending on the idea of communications between living entities such as birds' flocks, Ant Colony, and fish, which means deliberates the group behavior evolving through self-organizing of population individuals. SI has been stimulated via the surveillance of group behavior in its populations in nature because their behavior appears to have the ability to solve complex tasks and optimization problems. The fitness function which is based on SI has been improved to solve combinatorial and mathematical optimization problems by using these algorithms. This means, these techniques work based on the behaviors of individuals in their population so the observation, swam algorithms can be employed for solving the different problems in various applications such as in the medical systems or to enhance the performance of other application systems. In this paper, some swarm intelligent methodologies are reviewed and concerns with their applications in some areas are mentioned.
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References
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[2] Arushi Gupta and Smriti Srivastava, "Comparative Analysis of Ant Colony and Particle Swarm Optimization Algorithms for Distance Optimization, Procedia Computer Science 173 (2020) 245–253.
[3] Ankit Datey and et.al, "Review on Artificial Bee Colony", Volume 7 Issue No.4, International Journal of Engineering Science and Computing, April 2017.
[4] Julius Odili and et.al,"A comparative evaluation of swarm intelligence techniques for solving combinatorial optimization problems", International Journal of Advanced Robotic Systems, 2017. DOI: 10.1177/1729881417705969.
[5] Norfadzlia Mohd Yusof and et.al.," Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification", APPLIED ARTIFICIAL INTELLIGENCE,2021, VOL. 35, NO. 12, 914–932,
https://doi.org/10.1080/08839514.2021.1966882.
[6] Yousef Qawqzeh and et.al, "A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing Environments", (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.696, (2021).
[7] Sharandeep Singh ,A Review on Particle Swarm Optimization Algorithm, International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014.
[8] Wang Chun-Feng and et.al, "Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization", Mathematical Problems in Engineering, Volume 2014, 2014.
[9] Aradhana Dahiya and Shabnam Sangwan,"Literature Review on Genetic Algorithm", International Journal of Research, Volume 05 Issue 16, 2018.
[10] Abdelazim G. Hussien and et.al." Binary Whale Optimization Algorithm for Dimensionality Reduction", Mathematics, doi: 10.3390/math8101821, 2020.
[11]Ahmed Samy and et.al," An efficient binary whale optimization algorithm with optimum path forest for feature selection", International Journal of Computer Applications in Technology, DOI: 10.1504/IJCAT.2020.107913,2020.
[12] Heba F. Eid and Azah Kamilah Muda, " Adjustive Reciprocal Whale Optimization Algorithm for Wrapper Attribute Selection and Classification", I.J. Image, Graphics and Signal Processing, 2019,
DOI: 10.5815/ijigsp.2019.03.03.
[13]Ah. E. Hegazy and et.al, "Improved salp swarm algorithm for feature selection", Journal of King Saud University – Computer and Information Sciences 32 (2020).
[14]Miodrag Zivkovic and et.al, " Novel Improved Salp Swarm Algorithm: An Application for Feature Selection", Sensors, DOI: 10.3390/s22051711, 2022.
[15] Chao Zhou and et.al, "An Improved Bird Swarm Algorithm with Adaptive Characteristics ", International Symposium on Communication Engineering & Computer Science (CECS), Advances in Computer Science Research, volume 86, 2018.
[16] LING XIANG and et.al, " Forecasting Short-Term Wind Speed Based on IEWT-LSSVM Model Optimized by Bird Swarm Algorithm", SPECIAL SECTION ON ADVANCES IN PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT, VOLUME 7, Digital Object Identifier 10.1109/ACCESS.2019.2914251, 2019.
[17] Rizk M. Rizk Allah and et.al, "Enhanced Tunicate Swarm Algorithm for Solving Large Scale Nonlinear Optimization Problems", International Journal of Computational Intelligence Systems, https://doi.org/10.1007/s44196-021-00039-4.
[18] Widi Aribowo and et.al, "Tunicate Swarm Algorithm-Neural Network for Adaptive Power System Stabilizer Parameter", Science & Technology Asia, Vol.26 No.3, 2021, doi: 10.14456/scitechasia.2021.46.
[19] Chandirasekaran, D. and Jayabarathi, T. (2017); “Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach”; Cluster Computing; Springer Science+Business Media, LLC, part of Springer Nature; vol. 22; pp.11351–11361; doi:https://doi.org/10.1007/s10586-017-1392-4.
[20] Eid, H. F. (2018); “Binary whale optimisation: an effective swarm algorithm for feature selection”; Int. J. Metaheuristics; vol. 7; no. 1.; pp. 67-79.
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[22] Yang, H. and Et al. (2019); “An adaptive bird swarm algorithm with irregular random flight and its application”; Journal of Computational Science; vol. 35; pp. 57–65; Elsevier B.V.; doi:https://doi.org/10.1016/j.jocs.2019.06.004
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[24] Bairathi D. and Gopalani D. (2019); “Numerical optimization and feed forward neural networks training using an improved optimization algorithm: multiple leader salp swarm algorithm”; Springer-Verlag GmbH Germany, part of Springer Nature; vol. 14; pp. 1233–1249; doi:https://doi.org/10.1007/s12065-019-00269-8.
[25] Abualigah L. and Et al. (2020); “Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications”; Engineering with Computers; Springer-Verlag London Ltd., part of Springer Nature; doi:https://doi.org/10.1007/s00366-020-01067-y.
[26] Aljarah, I. and Et al. (2020); “A Dynamic Locality Multi-Objective Salp Swarm Algorithm for Feature Selection”; Computers & Industrial Engineering; Elsevier Ltd.; vol. 147; doi:https://doi.org/10.1016/j.cie.2020.106628.
[27] Kaur, S. and Et al. (2020); “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization”; Engineering Applications of Artificial Intelligence 90 103541; Elsevier Ltd.; vol. 90; doi: https://doi.org/10.1016/j.engappai.2020.103541
[28] QIAO, W. and YANG, Z. (2020); “An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function”; IEEE Access, vol. 8; pp. 2073-2089; doi: 10.1109/ACCESS.2019.2958456
[29] Braik, M. and Et al. (2021); “A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm”; Soft Computing; vol. 25; pp. 181–206; doi: https://doi.org/10.1007/s00500-020-05130-0
[30] Zhang, H. and Et al. (2021); “Ensemble mutation-driven salp swarm algorithm with restart mechanism: Framework and fundamental analysis”; Expert Systems with Applications; vol. 165; doi:https://doi.org/10.1016/j.eswa.2020.113897
[31] Xia, J. and Et al. (2022); “Adaptive Barebones Salp Swarm Algorithm with Quasi oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis”; Journal of Bionic Engineering; vol. 19; pp. 240–256; doi:https://doi.org/10.1007/s42235-021-00114-8
[32] Yi, W. (2022); “Forecast of agricultural water resources demand based on particle swarm algorithm”; Acta Agriculturae Scandinavica, Section B — Soil & Plant Science; vol. 72; no.:1; pp. 30-42, DOI: 10.1080/09064710.2021.19903867
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Published
2022-06-04
How to Cite
Ibrahim, N. khalid, & Shati, N. M. (2022). Review on swarm intelligent techniques and their applications in different area. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(2), Comp Page 23–45. https://doi.org/10.29304/jqcm.2022.14.2.935
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Computer Articles
