Comprehensive Study of Population Based Algorithms

Yam Krishna Poudel; Jeewan Phuyal; Rajib Kumar

doi:doi:10.11648/j.ajcst.20240704.17

Research Article |

| Peer-Reviewed

Comprehensive Study of Population Based Algorithms

Yam Krishna Poudel^*

, Jeewan Phuyal, Rajib Kumar

Published in American Journal of Computer Science and Technology (Volume 7, Issue 4)

Received: 20 November 2024 Accepted: 7 December 2024 Published: 23 December 2024

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates and high fitness scores. The investigation examines each algorithm's unique features, optimization properties, and operational paradigms, conducting broad comparative analyses against conventional methods, such as search history, fitness functions and to express their superiority. The study also assesses their relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. The findings show the transformative potential of these algorithms and offering valuable wisdom for future research to enhance and broaden upon these methodologies. This finding assists as a guiding for researchers to enable inventive solutions based in natural algorithms and advancing the field of optimization.

Published in	American Journal of Computer Science and Technology (Volume 7, Issue 4)
DOI	10.11648/j.ajcst.20240704.17
Page(s)	195-217
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Meta Heuristic Algorithms, Particle Swarm Optimization, Spider Monkey Optimization, Grey Wolf Optimization, Cuckoo Search Optimization, Grasshopper Optimization Algorithm, Ant Colony Optimization

References

[1]	A. Ahmad et al., “An Optimized Home Energy Management System with Integrated R enewable Energy and Storage Resources,” Energies, vol. 10, no. 4, p. 549, Apr. 2017, https://doi.org/10.3390/en10040549
[2]	J. U et al., “Particle Swarm Optimization based Spatiallocation Allocation of Urban Parks,” 2014 Third Int. Conf. Agro-Geoinformatics, no. March, pp. 1–6, 2014.
[3]	W. Al-Saedi, S. W. lachowicz, D. Habibi, and O. Bass, “Power flow control in grid-connected microgrid operation using Particle Swarm Optimization under variableload conditions,” Int. J. Electr. Power Energy Syst., vol. 49, pp. 76–85, Jul. 2013, https://doi.org/10.1016/j.ijepes.2012.12.017
[4]	S. M. Haakonsen, S. H. Dyvik, M. luczkowski, and A. Rønnquist, “A Grasshopper Plugin for Finite Element Analysis with Solid Elements and Its Application on Gridshell Nodes,” Appl. Sci., vol. 12, no. 12, 2022, https://doi.org/10.3390/app12126037
[5]	S. R. Kamel and R. Yaghoubzadeh, “Feature selection using grasshopper optimization algorithm in diagnosis of diabetes disease,” Informatics Med. Unlocked, vol. 26, p. 100707, 2021, https://doi.org/10.1016/j.imu.2021.100707
[6]	J. J. Kim and J. J. lee, “Trajectory optimization with particle swarm optimization for manipulator motion planning,” IEEE Trans. Ind. Informatics, vol. 11, no. 3, pp. 620–631, 2015, https://doi.org/10.1109/TII.2015.2416435
[7]	P. Hou, W. Hu, M. Soltani, and Z. Chen, “Optimized Placement of Wind Turbines inlarge-Scale Offshore Wind Farm Using Particle Swarm Optimization Algorithm,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1272–1282, Oct. 2015, https://doi.org/10.1109/TSTE.2015.2429912
[8]	Z. Yan, Y. li, and M. Eslami, “Maximizing micro-grid energy output with modified chaos grasshopper algorithms,” Heliyon, vol. 10, no. 1, p. e23980, Jan. 2024, https://doi.org/10.1016/j.heliyon.2024.e23980
[9]	H. H. Inbarani, A. T. Azar, and G. Jothi, “Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis,” Comput. Methods Programs Biomed., vol. 113, no. 1, pp. 175–185, Jan. 2014, https://doi.org/10.1016/j.cmpb.2013.10.007
[10]	S. Abbasi et al., “Design optimization of concrete gravity dams using grasshopper optimization algorithm,” Innov. Infrastruct. Solut., vol. 9, no. 12, p. 453, Dec. 2024, https://doi.org/10.1007/s41062-024-01741-w
[11]	M. Prabakaran, M. K. Bhole, V. Kalpana, S. Dixit, K. Divya, and D. A. Chauhan, “Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extremelearning Approach,” in 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), IEEE, Dec. 2023, pp. 1092–1097. https://doi.org/10.1109/ICIMIA60377.2023.10426121
[12]	D. Kumari, A. Sinha, S. Dutta, and P. Pranav, “Optimizing neural networks using spider monkey optimization algorithm for intrusion detection system,” Sci. Rep., vol. 14, no. 1, p. 17196, Jul. 2024, https://doi.org/10.1038/s41598-024-68342-6
[13]	T. Hosseinalizadeh, S. M. Salamati, S. A. Salamati, and G. B. Gharehpetian, “Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for Turbine-Governor and Excitation System,” IEEE Trans. Energy Convers., vol. 34, no. 2, pp. 585–593, Jun. 2019, https://doi.org/10.1109/TEC.2018.2868747
[14]	A. Gálvez, I. Fister, S. Deb, I. Fister, and A. Iglesias, “Hybrid GA-PSO method withlocal search and image clustering for automatic IFS image reconstruction of fractal colored images,” Neural Comput. Appl., Nov. 2023, https://doi.org/10.1007/s00521-023-08954-7
[15]	S. Biswas, K. Mandal, D. Pramanik, N. Roy, R. Biswas, and A.. Kuar, “Prediction and optimization of Nd: YAGlaser transmission micro-channelling on PMMA employing an artificial neural network model,” Infrared Phys. Technol., vol. 137, p. 105121, Mar. 2024, https://doi.org/10.1016/j.infrared.2024.105121
[16]	A. I. Omar, Z. M. Ali, S. H. E. Abdel Aleem, E. E. A. El-Zahab, and A. M. Sharaf, “A dynamic switched compensation scheme for grid-connected wind energy systems using cuckoo search algorithm,” Int. J. Energy Convers., vol. 7, no. 2, pp. 64–74, 2019, https://doi.org/10.15866/irecon.v7i2.16895
[17]	R. Sridhar, C. Subramani, and S. Pathy, “A grasshopper optimization algorithm aided maximum power point tracking for partially shaded photovoltaic systems,” Comput. Electr. Eng., vol. 92, p. 107124, Jun. 2021, https://doi.org/10.1016/j.compeleceng.2021.107124
[18]	S. R. Salkuti, “Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm,” Int. J. Electr. Comput. Eng., vol. 7, no. 5, p. 2349, Oct. 2017, https://doi.org/10.11591/ijece.v7i5.pp2349-2356
[19]	A. A. Ghavifekr, A. Mohammadzadeh, and J. F. Ardashir, “Optimal Placement and Sizing of Energy-related Devices in Microgrids Using Grasshopper Optimization Algorithm,” in 2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), IEEE, Feb. 2021, pp. 1–4. https://doi.org/10.1109/PEDSTC52094.2021.9405951
[20]	Li Yancang, Cheng Fangmeng, and J. Suo, “Improved ACO inspired bylogistics and distribution problem,” in 2010 2nd International Conference on Advanced Computer Control, IEEE, 2010, pp. 369–371. https://doi.org/10.1109/ICACC.2010.5487086
[21]	R. DIAF, C. TOLBA, and A. Nait Sidi Moh, “Traffic Urban Control Using an Intelligent PSO Algorithm Based on Integrated Approach,” Alger. J. Signals Syst., vol. 5, no. 1, pp. 1–9, Mar. 2020, https://doi.org/10.51485/ajss.v5i1.89
[22]	C. H. Ram Jethmalani, S. P. Simon, K. Sundareswaran, P. S. R. Nayak, and N. P. Padhy, “Auxiliary Hybrid PSO-BPNN-Based Transmission Systemloss Estimation in Generation Scheduling,” IEEE Trans. Ind. Informatics, vol. 13, no. 4, pp. 1692–1703, Aug. 2017, https://doi.org/10.1109/TII.2016.2614659
[23]	P. C. S. Rao, P. K. Jana, and H. Banka, “A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks,” Wirel. Networks, vol. 23, no. 7, pp. 2005–2020, Oct. 2017, https://doi.org/10.1007/s11276-016-1270-7
[24]	A. K. Sangaiah, A. A. R. Hosseinabadi, M. B. Shareh, S. Y. B. Rad, A. Zolfagharian, and N. Chilamkurti, “IoT resource allocation and optimization based on heuristic algorithm,” Sensors (Switzerland), vol. 20, no. 2, 2020, https://doi.org/10.3390/s20020539
[25]	L. Xudong, l. Shuo, and F. Qingwu, “Prediction of Building Heating and Coolingload Based on IPSO-LSTM Neural Network,” in 2020 Chinese Automation Congress (CAC), IEEE, Nov. 2020, pp. 1085–1090. https://doi.org/10.1109/CAC51589.2020.9327849
[26]	H. E. Mostafa, M. A. El-Sharkawy, A. A. Emary, and K. Yassin, “Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system,” Int. J. Electr. Power Energy Syst., vol. 34, no. 1, pp. 57–65, Jan. 2012, https://doi.org/10.1016/j.ijepes.2011.09.005
[27]	M. H. Ibrahim, S. P. Ang, M. N. Dani, M. I. Rahman, R. Petra, and S. M. Sulthan, “Optimizing Step-Size of Perturb & Observe and Incremental Conductance MPPT Techniques Using PSO for Grid-Tied PV System,” IEEE Access, vol. 11, pp. 13079–13090, 2023, https://doi.org/10.1109/ACCESS.2023.3242979
[28]	Q. S. Khalid et al., “Hybrid Particle Swarm Algorithm for Products’ Scheduling Problem in Cellular Manufacturing System,” Symmetry (Basel)., vol. 11, no. 6, p. 729, May 2019, https://doi.org/10.3390/sym11060729
[29]	G.-C. luh and C.-Y. lin, “Optimal design of truss-structures using particle swarm optimization,” Comput. Struct., vol. 89, no. 23–24, pp. 2221–2232, Dec. 2011, https://doi.org/10.1016/j.compstruc.2011.08.013
[30]	A. Agrawal, D. Garg, D. Popli, A. Banerjee, A. Raj, and I. Dikshit, “A review of spider monkey optimization: modification and its biomedical application,” Int. J. Interact. Des. Manuf., Dec. 2023, https://doi.org/10.1007/s12008-023-01671-4
[31]	B. Isong and O. Kgote, “Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems,” 2024.
[32]	Y. lan, Q. Chen, l. Zhang, and R. long, “Model Predictive Control Based On Spider monkey optimization Algorithm of Interleaved Parallel Bidirectional DC-DC Converter,” pp. 50–55, 2020.
[33]	D. Tripathy, B. K. Sahu, N. B. D. Choudhury, and S. Dawn, “Spider monkey optimization based cascade controller forlFC of a hybrid power system.,” pp. 747–753, 2018.
[34]	R. K. Sanapala, “An Optimized Energy Efficient Routing for Wireless Sensor Network using Improved Spider Monkey Optimization Algorithm,” vol. 15, no. 1, pp. 188–197, 2022, https://doi.org/10.22266/ijies2022.0228.18
[35]	R. Alkanhel, A. A. Abdelhamid, A. Ibrahim, M. A. Alohali, M. Abotaleb, and D. S. Khafaga, “Metaheuristic Optimization,” 2023, https://doi.org/10.32604/cmc.2023.033273
[36]	X. Zhang, Y. M. Xie, and S. Zhou, “A nodal-based evolutionary optimization algorithm for frame structures,” Comput. Civ. Infrastruct. Eng., vol. 38, no. 3, pp. 288–306, 2023, https://doi.org/10.1111/mice.12834
[37]	G. Nirmalapriya, V. Agalya, R. Regunathan, and M. Belsam Jeba Ananth, “Fractional Aquila spider monkey optimization based deeplearning network for classification of brain tumor,” Biomed. Signal Process. Control, vol. 79, p. 104017, Jan. 2023, https://doi.org/10.1016/j.bspc.2022.104017
[38]	W. Sultana and S. D. S. Jebaseelan, “Optimal allocation of solar PV and wind energy power for radial distribution system using spider monkey optimization,” Sustain. Comput. Informatics Syst., vol. 42, p. 100986, Apr. 2024, https://doi.org/10.1016/j.suscom.2024.100986
[39]	N. Khare et al., “SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection,” Electronics, vol. 9, no. 4, p. 692, Apr. 2020, https://doi.org/10.3390/electronics9040692
[40]	A. Kumar Kashyap and D. R. Parhi, “Multi-objective trajectory planning of humanoid robot using hybrid controller for multi-target problem in complex terrain,” Expert Syst. Appl., vol. 179, p. 115110, Oct. 2021, https://doi.org/10.1016/j.eswa.2021.115110
[41]	P. Vijayalakshmi and D. Karthika, “Hybrid dual-channel convolution neural network (DCCNN) with spider monkey optimization (SMO) for cyber security threats detection in internet of things,” Meas. Sensors, vol. 27, p. 100783, Jun. 2023, https://doi.org/10.1016/j.measen.2023.100783
[42]	B. Sahu, A. Panigrahi, B. Dash, P. K. Sharma, and A. Pati, “A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection,” Int. J. Reconfigurable Embed. Syst., vol. 12, no. 3, pp. 360–375, 2023, https://doi.org/10.11591/ijres.v12.i3.pp360-375
[43]	M. Montalvo-Martel, A. Ochoa-Zezzatti, E. Carrum, and D. Barzaga, “Design of an Urban Transport Network for the Optimallocation of Bus Stops in a Smart City Based on a Big Data Model and Spider Monkey Optimization Algorithm,” 2021, pp. 167–201. https://doi.org/10.1007/978-3-030-68655-0_9
[44]	S. Mirjalili, S. M. Mirjalili, and A. lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014, https://doi.org/10.1016/j.advengsoft.2013.12.007
[45]	A. Yahiaoui, F. Fodhil, K. Benmansour, M. Tadjine, and N. Cheggaga, “Grey wolf optimizer for optimal design of hybrid renewable energy system PV-Diesel Generator-Battery: Application to the case of Djanet city of Algeria,” Sol. Energy, vol. 158, pp. 941–951, Dec. 2017, https://doi.org/10.1016/j.solener.2017.10.040
[46]	X. li and Y.-X. Guo, “The Grey Wolf Optimizer for Antenna Optimization Designs: Continuous, binary, single-objective, and multiobjective implementations,” IEEE Antennas Propag. Mag., vol. 64, no. 6, pp. 29–40, Dec. 2022, https://doi.org/10.1109/MAP.2021.3127798
[47]	Q. Al-Tashi et al., “Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification,” IEEE Access, vol. 8, pp. 106247–106263, 2020, https://doi.org/10.1109/ACCESS.2020.3000040
[48]	S. KILIÇARSLAN, “PSO + GWO: a hybrid particle swarm optimization and Grey Wolf optimization based Algorithm for fine-tuning hyper-parameters of convolutional neural networks for Cardiovascular Disease Detection,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 1, pp. 87–97, Jan. 2023, https://doi.org/10.1007/s12652-022-04433-4
[49]	R. Debbarma* and D. C. Nandi*, “Maximum Power Point Tracking using Grey Wolf Technique Under Fast-Changing Irradiance,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 12, pp. 365–371, Sep. 2020, https://doi.org/10.35940/ijitee.K7838.0991120
[50]	Z. Wang, Z. Jin, Z. Yang, W. Zhao, and M. Trik, “Increasing efficiency for routing in internet of things using Binary Gray Wolf Optimization and fuzzylogic,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, no. 9, p. 101732, Oct. 2023, https://doi.org/10.1016/j.jksuci.2023.101732
[51]	S. Yadav, S. K. Nagar, and A. Mishra, “Tuning of parameters of PID controller using Grey Wolf Optimizer,” SSRN Electron. J., 2020, https://doi.org/10.2139/ssrn.3575432
[52]	I. I. Novendra, I. M. Wirawan, A. Kusumawardana, and A. K. latt, “Optimization ofload frequency control using grey wolf optimizer in micro hydro power plants,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 14, no. 2, pp. 166–176, Dec. 2023, https://doi.org/10.14203/j.mev.2023.v14.166-176
[53]	S. Kumar Chandar, “RETRACTED ARTICLE: Grey Wolf optimization-Elman neural network model for stock price prediction,” Soft Comput., vol. 25, no. 1, pp. 649–658, Jan. 2021, https://doi.org/10.1007/s00500-020-05174-2
[54]	Y. Qiu, X. Yang, and S. Chen, “An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems,” Sci. Rep., vol. 14, no. 1, p. 14190, Jun. 2024, https://doi.org/10.1038/s41598-024-64526-2
[55]	A. Pawlowski, S. Romaniuk, Z. Kulesza, and M. Petrovic, “Trajectory optimization usinglearning from demonstration with meta-heuristic grey wolf algorithm,” IAES Int. J. Robot. Autom., vol. 11, no. 4, p. 263, Dec. 2022, https://doi.org/10.11591/ijra.v11i4.pp263-277
[56]	K. Jaiswal and V. Anand, “A Grey-Wolf based Optimized Clustering approach to improve QoS in wireless sensor networks for IoT applications,” Peer-to-Peer Netw. Appl., vol. 14, no. 4, pp. 1943–1962, Jul. 2021, https://doi.org/10.1007/s12083-021-01099-1
[57]	J. Y. An, Z. H. You, Y. Zhou, and D. F. Wang, “Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer–Based Relevance Vector Machine,” Evol. Bioinforma., vol. 15, 2019, https://doi.org/10.1177/1176934319844522
[58]	S. li and F. Wang, “Research on optimization of improved gray wolf optimization-extremelearning machine algorithm in vehicle route planning,” Discret. Dyn. Nat. Soc., vol. 2020, 2020, https://doi.org/10.1155/2020/8647820
[59]	H. Alkhraisat, l. M. Dalbah, M. A. Al-Betar, M. A. Awadallah, K. Assaleh, and M. Deriche, “Size Optimization of Truss Structures Using Improved Grey Wolf Optimizer,” IEEE Access, vol. 11, no. February, pp. 13383–13397, 2023, https://doi.org/10.1109/ACCESS.2023.3243164
[60]	A. S. Mohammed and A. Dodo, “Load Frequency Control of One and Two Areas Power System Using Grasshopper Optimization Based Fractional Order PID Controller,” Control Syst. Optim. lett., vol. 1, no. 1, pp. 32–40, Apr. 2023, https://doi.org/10.59247/csol.v1i1.12
[61]	T. Tamilarasan and M. V. Suganyadevi, “An improvement of Global Maximum Power Point Tracking Using a Novel Grasshopper Optimisation Algorithm of Photovoltaic System,” Iran. J. Sci. Technol. Trans. Electr. Eng., vol. 48, no. 2, pp. 929–943, Jun. 2024, https://doi.org/10.1007/s40998-024-00709-x
[62]	A. Abdulrahman,.. Z. M., A. M. Zaki, F. H. H. Rizk, M. M. Eid, and E.-S. M. EL EL-Kenawy, “Exploring Optimization Algorithms: A Review of Methods and Applications,” J. Artif. Intell. Metaheuristics, vol. 7, no. 2, pp. 08–17, 2024, https://doi.org/10.54216/JAIM.070201
[63]	S. Kolli and B. R. Parvathala, “A Novel Assessment oflung Cancer Classification System Using Binary Grasshopper with Artificial Bee Optimisation Algorithm with Double Deep Neural Network Classifier,” J. Inst. Eng. Ser. B, vol. 105, no. 5, pp. 1129–1143, Oct. 2024, https://doi.org/10.1007/s40031-024-01027-w
[64]	J. Xia et al., “Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis,” Comput. Biol. Med., vol. 143, p. 105206, Apr. 2022, https://doi.org/10.1016/j.compbiomed.2021.105206
[65]	X. Yue, H. Zhang, and H. Yu, “A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization,” IEEE Access, vol. 8, pp. 5928–5960, 2020, https://doi.org/10.1109/ACCESS.2019.2963679
[66]	S. M. Haakonsen, S. H. Dyvik, M. luczkowski, and A. Rønnquist, “A Grasshopper Plugin for Finite Element Analysis with Solid Elements and Its Application on Gridshell Nodes,” Appl. Sci., vol. 12, no. 12, p. 6037, Jun. 2022, https://doi.org/10.3390/app12126037
[67]	M. Sabouri and A. Sepidbar, “U-Net-based integrated framework for pavement crack detection and zone-based scoring,” Int. J. Pavement Eng., vol. 25, no. 1, Dec. 2024, https://doi.org/10.1080/10298436.2024.2308183
[68]	C. Waibel, l. Bystricky, A. Kubilay, R. Evins, and J. Carmeliet, “Validation of Grasshopper-based Fast Fluid Dynamics for Air Flow around Buildings in Early Design Stage,” Aug. 2017. https://doi.org/10.26868/25222708.2017.582
[69]	T. Wortmann, “Model-based Optimization for Architectural Design: Optimizing Daylight and Glare in Grasshopper,” Technol. + Des., vol. 1, no. 2, pp. 176–185, Nov. 2017, https://doi.org/10.1080/24751448.2017.1354615
[70]	A. Maksoud, H. B. Al-Beer, A. A. Hussien, S. Dirar, E. Mushtaha, and M. W. Yahia, “Computational Design for Futuristic Environmentally Adaptive Building Forms and Structures,” Archit. Eng., vol. 8, no. 1, pp. 13–24, 2023, https://doi.org/10.23968/2500-0055-2023-8-1-13-24
[71]	M. Braik et al., “Predicting Surface Ozonelevels in Eastern Croatia: leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm,” Water, Air, Soil Pollut., vol. 235, no. 10, p. 655, Oct. 2024, https://doi.org/10.1007/s11270-024-07378-w
[72]	S. Polepaka et al., “Optimized convolutional neural network using grasshopper optimization technique for enhanced heart disease prediction,” Cogent Eng., vol. 11, no. 1, p., 2024, https://doi.org/10.1080/23311916.2024.2423847
[73]	S. Selvarajan, A comprehensive study on modern optimization techniques for engineering applications, vol. 57, no. 8. Springer Netherlands, 2024. https://doi.org/10.1007/s10462-024-10829-9
[74]	B. Chithra and R. Nedunchezhian, “Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3236–3246, Jun. 2022, https://doi.org/10.1016/j.jksuci.2020.06.011
[75]	S. Afanasyeva, J. Saari, O. Pyrhönen, and J. Partanen, “Cuckoo search for wind farm optimization with auxiliary infrastructure,” Wind Energy, vol. 21, no. 10, pp. 855–875, Oct. 2018, https://doi.org/10.1002/we.2199
[76]	R. Zhang, X. Jiang, and R. li, “Improved decomposition-based multi-objective cuckoo search algorithm for spectrum allocation in cognitive vehicular network,” Phys. Commun., vol. 34, pp. 301–309, Jun. 2019, https://doi.org/10.1016/j.phycom.2018.06.003
[77]	S. Haghdoost, M. H. Niksokhan, M. G. Zamani, and M. R. Nikoo, “Optimal wasteload allocation in river systems based on a new multi-objective cuckoo optimization algorithm,” Environ. Sci. Pollut. Res., vol. 30, no. 60, pp. 126116–126131, Nov. 2023, https://doi.org/10.1007/s11356-023-31058-7
[78]	H. Xue, “Adaptive Cultural Algorithm-Based Cuckoo Search for Time-Dependent Vehicle Routing Problem with Stochastic Customers Using Adaptive Fractional Kalman Speed Prediction,” Math. Probl. Eng., vol. 2020, 2020, https://doi.org/10.1155/2020/7258780
[79]	S. Sengar and X. liu, “Optimal electricalload forecasting for hybrid renewable resources through a hybrid memetic cuckoo search approach,” Soft Comput., vol. 24, no. 17, pp. 13099–13114, Sep. 2020, https://doi.org/10.1007/s00500-020-04727-9
[80]	A. Zadeh Shirazi, M. Hatami, M. Yaghoobi, and S. J. Seyyed Mahdavi Chabok, “An Intelligent Approach to Predict Vibration Rate in a Real Gas Turbine,” Intell. Ind. Syst., vol. 2, no. 3, pp. 253–267, 2016, https://doi.org/10.1007/s40903-016-0057-6
[81]	F. Ahmadkhanlou and H. Adeli, “Optimum cost design of reinforced concrete slabs using neural dynamics model,” Eng. Appl. Artif. Intell., vol. 18, no. 1, pp. 65–72, 2005, https://doi.org/10.1016/j.engappai.2004.08.025
[82]	A. Kumar, S. S. Satyanarayana Reddy, G. B. Mahommad, B. Khan, and R. Sharma, “Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework,” Sci. Program., vol. 2022, pp. 1–11, May 2022, https://doi.org/10.1155/2022/2090681
[83]	R. Salgotra, N. Mittal, A. S. Almazyad, and A. W. Mohamed, “RGN: A Triple Hybrid Algorithm for Multi-level Image Segmentation with Type II Fuzzy Sets,” Ain Shams Eng. J., vol. 15, no. 11, p. 102997, Nov. 2024, https://doi.org/10.1016/j.asej.2024.102997
[84]	R. Salgotra and S. Mirjalili, “Multi-algorithm based evolutionary strategy with Adaptive Mutation Mechanism for Constraint Engineering Design Problems,” Expert Syst. Appl., vol. 258, p. 125055, Dec. 2024, https://doi.org/10.1016/j.eswa.2024.125055
[85]	M. Guerrero, O. Castillo, and M. García, “Cuckoo Search vialévy Flights and a Comparison with Genetic Algorithms,” 2015, pp. 91–103. https://doi.org/10.1007/978-3-319-10960-2_6
[86]	A. R. Yildiz, “Cuckoo search algorithm for the selection of optimal machining parameters in milling operations,” Int. J. Adv. Manuf. Technol., vol. 64, no. 1–4, pp. 55–61, Jan. 2013, https://doi.org/10.1007/s00170-012-4013-7
[87]	R. Salgotra, U. Singh, S. Saha, and A. H. Gandomi, “Self adaptive cuckoo search: Analysis and experimentation,” Swarm Evol. Comput., vol. 60, p. 100751, Feb. 2021, https://doi.org/10.1016/j.swevo.2020.100751
[88]	H. Tang, X. li, l. Meng, Z. Zhang, and S. Chen, “Process modeling and optimization inlaser drilling of bulk metallic glasses based on GABPNN and machine vision,” Opt.laser Technol., vol. 172, p. 110502, May 2024, https://doi.org/10.1016/j.optlastec.2023.110502
[89]	B. S. Anukeerthana, D. S. lavanya, V. Gurucharran, and R. Madhumathi, “Improving Agricultural Productivity and Water Usage Through Advanced ACO Technique,” in 2024 10th International Conference on Communication and Signal Processing (ICCSP), IEEE, Apr. 2024, pp. 93–97. https://doi.org/10.1109/ICCSP60870.2024.10544083
[90]	S. Ç. Öztürk and E. Ö. A. Aktan, “A Cultural Route Recommendation Based on Optimization Techniques in Urban Spaces,” Int. J. Sustain. Dev. Plan., vol. 19, no. 9, pp. 3417–3430, 2024, https://doi.org/10.18280/ijsdp.190912
[91]	S. Kashef and H. Nezamabadi-pour, “An advanced ACO algorithm for feature subset selection,” Neurocomputing, vol. 147, pp. 271–279, Jan. 2015 https://doi.org/10.1016/j.neucom.2014.06.067
[92]	T. Islam, M. E. Islam, and M. R. Ruhin, “An Analysis of Foraging and Echolocation Behavior of Swarm Intelligence Algorithms in Optimization: ACO, BCO and BA,” Int. J. Intell. Sci., vol. 08, no. 01, pp. 1–27, 2018, https://doi.org/10.4236/ijis.2018.81001
[93]	K. Jairam Naik, “A Dynamic ACO-Based Elasticload Balancer for Cloud Computing (D-ACOELB),” 2020, pp. 11–20. https://doi.org/10.1007/978-981-15-1097-7_2
[94]	J. Wu and H. Zou, “Harmonic detection technology based on ant colony optimization BP neural network,” J. Phys. Conf. Ser., vol. 2221, no. 1, p. 012058, May 2022, https://doi.org/10.1088/1742-6596/2221/1/012058
[95]	A. W. Wijayanto, S. Mariyah, and A. Purwarianti, “Enhancing clustering quality of fuzzy geographically weighted clustering using Ant Colony optimization,” Proc. 2017 Int. Conf. Data Softw. Eng. ICoDSE 2017, vol. 2018-January, pp. 1–6, 2017, https://doi.org/10.1109/ICODSE.2017.8285858
[96]	Z. Wan, Y. Guo, J. Yang, X. Wang, and J. li, “Logistics Routing Intelligence based on Improved Ant Colony Algorithm and Dijkstra Algorithm,” Front. Sci. Eng., vol. 4, no. 8, pp. 130–142, Aug. 2024, https://doi.org/10.54691/669kn656
[97]	Y. Gajpal and P. l. Abad, “Multi-ant colony system (MACS) for a vehicle routing problem with backhauls,” Eur. J. Oper. Res., vol. 196, no. 1, pp. 102–117, Jul. 2009, https://doi.org/10.1016/j.ejor.2008.02.025
[98]	B. Saeidian, M. S. Mesgari, B. Pradhan, and M. Ghodousi, “Optimizedlocation-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS,” ISPRS Int. J. Geo-Information, vol. 7, no. 8, p. 292, Jul. 2018, https://doi.org/10.3390/ijgi7080292
[99]	Y. Gao, J. Wang, and C. li, “Escape afterlove: Philoponella prominens optimizer and its application to 3D path planning,” Cluster Comput., vol. 28, no. 2, p. 81, Apr. 2025, https://doi.org/10.1007/s10586-024-04761-4
[100]	Sukono et al., “The effect of gross domestic product and population growth on CO2 emissions in Indonesia: An application of the ant colony optimisation algorithm and cobb-douglas model,” Int. J. Energy Econ. Policy, vol. 9, no. 4, pp. 313–319, 2019, https://doi.org/10.32479/ijeep.8011
[101]	M. R. Jabbarpour, H. Malakooti, R. M. Noor, N. B. Anuar, and N. Khamis, “Ant colony optimisation for vehicle traffic systems: applications and challenges,” Int. J. Bio-Inspired Comput., vol. 6, no. 1, p. 32, 2014, https://doi.org/10.1504/IJBIC.2014.059970
[102]	A. Tahir et al., “Hybrid HP-BOA: An Optimized Framework for Reliable Storage of Cloud Data Using Hybrid Meta-Heuristic Algorithm,” Appl. Sci., vol. 13, no. 9, p. 5346, Apr. 2023, https://doi.org/10.3390/app13095346

Cite This Article

Plain Text BibTeX RIS

APA Style

Poudel, Y. K., Phuyal, J., Kumar, R. (2024). Comprehensive Study of Population Based Algorithms. American Journal of Computer Science and Technology, 7(4), 195-217. https://doi.org/10.11648/j.ajcst.20240704.17

Copy | Download

ACS Style

Poudel, Y. K.; Phuyal, J.; Kumar, R. Comprehensive Study of Population Based Algorithms. Am. J. Comput. Sci. Technol. 2024, 7(4), 195-217. doi: 10.11648/j.ajcst.20240704.17

Copy | Download

AMA Style

Poudel YK, Phuyal J, Kumar R. Comprehensive Study of Population Based Algorithms. Am J Comput Sci Technol. 2024;7(4):195-217. doi: 10.11648/j.ajcst.20240704.17

Copy | Download

@article{10.11648/j.ajcst.20240704.17,
  author = {Yam Krishna Poudel and Jeewan Phuyal and Rajib Kumar},
  title = {Comprehensive Study of Population Based Algorithms
},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {4},
  pages = {195-217},
  doi = {10.11648/j.ajcst.20240704.17},
  url = {https://doi.org/10.11648/j.ajcst.20240704.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240704.17},
  abstract = {The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates and high fitness scores. The investigation examines each algorithm's unique features, optimization properties, and operational paradigms, conducting broad comparative analyses against conventional methods, such as search history, fitness functions and to express their superiority. The study also assesses their relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. The findings show the transformative potential of these algorithms and offering valuable wisdom for future research to enhance and broaden upon these methodologies. This finding assists as a guiding for researchers to enable inventive solutions based in natural algorithms and advancing the field of optimization.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Comprehensive Study of Population Based Algorithms

AU  - Yam Krishna Poudel
AU  - Jeewan Phuyal
AU  - Rajib Kumar
Y1  - 2024/12/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ajcst.20240704.17
DO  - 10.11648/j.ajcst.20240704.17
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 195
EP  - 217
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20240704.17
AB  - The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates and high fitness scores. The investigation examines each algorithm's unique features, optimization properties, and operational paradigms, conducting broad comparative analyses against conventional methods, such as search history, fitness functions and to express their superiority. The study also assesses their relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. The findings show the transformative potential of these algorithms and offering valuable wisdom for future research to enhance and broaden upon these methodologies. This finding assists as a guiding for researchers to enable inventive solutions based in natural algorithms and advancing the field of optimization.

VL  - 7
IS  - 4
ER  -

Copy | Download

Author Information

Yam Krishna Poudel

Department of Computer Science Engineering, RIMT University, Punjab, India

Contact Email

http://orcid.org/0000-0002-9438-0395
Jeewan Phuyal

Department of Electrical and Electronics, Nepal Engineering College, Pokhara University, Pokhara, Nepal

Contact Email
Rajib Kumar

Department of Computer Science Engineering, RIMT University, Punjab, India

Contact Email

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Poudel, Y. K., Phuyal, J., Kumar, R. (2024). Comprehensive Study of Population Based Algorithms. American Journal of Computer Science and Technology, 7(4), 195-217. https://doi.org/10.11648/j.ajcst.20240704.17

Copy | Download

ACS Style

Poudel, Y. K.; Phuyal, J.; Kumar, R. Comprehensive Study of Population Based Algorithms. Am. J. Comput. Sci. Technol. 2024, 7(4), 195-217. doi: 10.11648/j.ajcst.20240704.17

Copy | Download

AMA Style

Poudel YK, Phuyal J, Kumar R. Comprehensive Study of Population Based Algorithms. Am J Comput Sci Technol. 2024;7(4):195-217. doi: 10.11648/j.ajcst.20240704.17

Copy | Download

@article{10.11648/j.ajcst.20240704.17,
  author = {Yam Krishna Poudel and Jeewan Phuyal and Rajib Kumar},
  title = {Comprehensive Study of Population Based Algorithms
},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {4},
  pages = {195-217},
  doi = {10.11648/j.ajcst.20240704.17},
  url = {https://doi.org/10.11648/j.ajcst.20240704.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240704.17},
  abstract = {The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates and high fitness scores. The investigation examines each algorithm's unique features, optimization properties, and operational paradigms, conducting broad comparative analyses against conventional methods, such as search history, fitness functions and to express their superiority. The study also assesses their relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. The findings show the transformative potential of these algorithms and offering valuable wisdom for future research to enhance and broaden upon these methodologies. This finding assists as a guiding for researchers to enable inventive solutions based in natural algorithms and advancing the field of optimization.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Comprehensive Study of Population Based Algorithms

AU  - Yam Krishna Poudel
AU  - Jeewan Phuyal
AU  - Rajib Kumar
Y1  - 2024/12/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ajcst.20240704.17
DO  - 10.11648/j.ajcst.20240704.17
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 195
EP  - 217
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20240704.17
AB  - The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates and high fitness scores. The investigation examines each algorithm's unique features, optimization properties, and operational paradigms, conducting broad comparative analyses against conventional methods, such as search history, fitness functions and to express their superiority. The study also assesses their relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. The findings show the transformative potential of these algorithms and offering valuable wisdom for future research to enhance and broaden upon these methodologies. This finding assists as a guiding for researchers to enable inventive solutions based in natural algorithms and advancing the field of optimization.

VL  - 7
IS  - 4
ER  -

Copy | Download