{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T04:27:00Z","timestamp":1728534420962},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Pers Commun"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11277-024-11584-4","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T06:02:03Z","timestamp":1728280923000},"page":"1893-1918","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Survey on Parallel Nature Inspired Algorithms"],"prefix":"10.1007","volume":"138","author":[{"given":"Lalit","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manish","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitul Kumar","family":"Ahirwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"11584_CR1","volume-title":"Swarm Intelligence","author":"J Kennedy","year":"2001","unstructured":"Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Academic Press."},{"key":"11584_CR2","volume-title":"Fundamentals of Computational Swarm Intelligence","author":"AP Engelbrecht","year":"2005","unstructured":"Engelbrecht, A. P. (2005). Fundamentals of Computational Swarm Intelligence. Wiley."},{"key":"11584_CR3","volume-title":"Swarm intelligence and bio-inspired computation theory and applications","author":"XS Yang","year":"2013","unstructured":"Yang, X. S., Cui, Z. H., Gandom, A. H., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation theory and applications. London, UK: Elsevier."},{"issue":"3","key":"11584_CR4","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1145\/937503.937505","volume":"35","author":"C Blum","year":"2003","unstructured":"Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35(3), 268\u2013308.","journal-title":"ACM computing surveys (CSUR)"},{"key":"11584_CR5","volume-title":"Handbook of metaheuristics","author":"FW Glover","year":"2006","unstructured":"Glover, F. W., & Kochenberger, G. A. (2006). Handbook of metaheuristics. Springer Science & Business Media."},{"issue":"1","key":"11584_CR6","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s11047-019-09769-z","volume":"20","author":"L Kumar","year":"2019","unstructured":"Kumar, L., & Bharti, K. K. (2019). A novel hybrid BPSO\u2013SCA approach for feature selection. Natural Computing, 20(1), 39\u201361. https:\/\/doi.org\/10.1007\/s11047-019-09769-z","journal-title":"Natural Computing"},{"issue":"5","key":"11584_CR7","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1002\/ett.1062","volume":"16","author":"G Di Caro","year":"2005","unstructured":"Di Caro, G., Ducatelle, F., & Gambardella, L. M. (2005). AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, 16(5), 443\u2013455.","journal-title":"European Transactions on Telecommunications"},{"key":"11584_CR8","unstructured":"Fister Jr, I., Yang, X.S., Fister, I., Brest, J., Fister, D. (2013). A brief review of nature-inspired algorithms for optimization.\u00a0arXiv preprint arXiv:1307.4186"},{"key":"11584_CR9","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2005) Engineering optimizations via nature-inspired virtual bee algorithms. In:\u00a0International Work-Conference on the Interplay between Natural and Artificial Computation, pp. 317\u2013323, Springer, Berlin, Heidelberg.","DOI":"10.1007\/11499305_33"},{"key":"11584_CR10","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.eswa.2016.04.018","volume":"59","author":"AK Kar","year":"2016","unstructured":"Kar, A. K. (2016). Bio inspired computing\u2013a review of algorithms and scope of applications. Expert Systems with Applications, 59, 20\u201332.","journal-title":"Expert Systems with Applications"},{"key":"11584_CR11","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.jocs.2020.101104","volume":"46","author":"XS Yang","year":"2020","unstructured":"Yang, X. S. (2020). Nature-inspired optimization algorithms\u2014challenges and open problems. Journal of Computational Science, 46, 101104.","journal-title":"Journal of Computational Science"},{"key":"11584_CR12","doi-asserted-by":"crossref","DOI":"10.1002\/0471739383","volume-title":"Parallel metaheuristics: a new class of algorithms","author":"E Alba","year":"2005","unstructured":"Alba, E. (2005). Parallel metaheuristics: a new class of algorithms. John Wiley & Sons."},{"issue":"4","key":"11584_CR13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.swevo.2011.10.001","volume":"1","author":"E Mezura-Montes","year":"2011","unstructured":"Mezura-Montes, E., & Coello, C. A. C. (2011). Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm and Evolutionary Computation, 1(4), 173\u2013194.","journal-title":"Swarm and Evolutionary Computation"},{"key":"11584_CR14","doi-asserted-by":"crossref","first-page":"103330","DOI":"10.1016\/j.engappai.2019.103330","volume":"87","author":"MH Sulaiman","year":"2020","unstructured":"Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2020). Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103330.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"11584_CR15","unstructured":"Davis, L. (1987) Genetic algorithms and simulated annealing: An overview. Genetic Algorithms and Simulated Annealing, 1."},{"issue":"4","key":"11584_CR16","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"S Rainer","year":"1997","unstructured":"Rainer, S., & Kenneth, P. (1997). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341\u2013359.","journal-title":"Journal of Global Optimization"},{"key":"11584_CR17","unstructured":"Goldberg, D. E., Holland, J. H. (1988) Genetic algorithms and machine learning."},{"issue":"6","key":"11584_CR18","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/2.294849","volume":"27","author":"M Srinivas","year":"1994","unstructured":"Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms\u2014a survey. Computer, 27(6), 17\u201326.","journal-title":"Computer"},{"key":"11584_CR19","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., & Price, K. (1997). Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 3341\u20133359.","journal-title":"Journal of Global Optimization"},{"key":"11584_CR20","doi-asserted-by":"publisher","unstructured":"Koza, J.R. (1999) Darwinian invention and problem solving by means of genetic programming. In:\u00a0IEEE SMC\u201999 Conference Proceedings. IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), Tokyo, Japan, 1999, pp. 604\u2013609 vol.3, https:\/\/doi.org\/10.1109\/ICSMC.1999.823281","DOI":"10.1109\/ICSMC.1999.823281"},{"key":"11584_CR21","doi-asserted-by":"publisher","unstructured":"Koza, J.R. (1990) Genetically breeding populations of computer programs to solve problems in artificial intelligence. In: Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, Herndon, VA, USA, 1990, pp. 819\u2013827, https:\/\/doi.org\/10.1109\/TAI.1990.130444","DOI":"10.1109\/TAI.1990.130444"},{"key":"11584_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2014.03.003","volume":"274","author":"M Amoretti","year":"2014","unstructured":"Amoretti, M. (2014). Evolutionary strategies for ultra-large-scale autonomic systems. Information Sciences, 274, 1\u201316.","journal-title":"Information Sciences"},{"issue":"3","key":"11584_CR23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10462-010-9191-9","volume":"35","author":"S Rana","year":"2011","unstructured":"Rana, S., Jasola, S., & Kumar, R. (2011). A review on particle swarm optimization algorithms and their applications to data clustering. Artificial Intelligence Review, 35(3), 211\u2013222.","journal-title":"Artificial Intelligence Review"},{"key":"11584_CR24","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R. (1995) Particle swarm optimization. In:\u00a0Proceedings of ICNN\u201995-International Conference on Neural Networks,\u00a0Vol. 4, pp. 1942\u20131948, IEEE.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"11584_CR25","unstructured":"Cervantes, A., Galv\u00e1n, I.M., Isasi, P. (2005) Binary particle swarm optimization in classification."},{"key":"11584_CR26","doi-asserted-by":"crossref","unstructured":"Jadon, S.S., Sharma, H., Kumar, E., Bansal, J.C. (2011) Application of binary particle swarm optimization in cryptanalysis of DES. In: Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS) December 20\u201322, 2011, pp. 1061\u20131071, Springer, India.","DOI":"10.1007\/978-81-322-0487-9_97"},{"issue":"22","key":"11584_CR27","doi-asserted-by":"crossref","first-page":"11042","DOI":"10.1016\/j.amc.2012.05.001","volume":"218","author":"JC Bansal","year":"2012","unstructured":"Bansal, J. C., & Deep, K. (2012). A modified binary particle swarm optimization for knapsack problems. Applied Mathematics and Computation, 218(22), 11042\u201311061.","journal-title":"Applied Mathematics and Computation"},{"key":"11584_CR28","doi-asserted-by":"crossref","unstructured":"Agneessens, J., Vandoorn, T., Meersman, B., Vandevelde, L. (2011) The use of binary particle swarm optimization to obtain a demand side management system. In: IET Renewable Power Generation Conference, Proceedings. IET events.","DOI":"10.1049\/cp.2011.0228"},{"key":"11584_CR29","doi-asserted-by":"crossref","unstructured":"Behjat, A.R., Mustapha, A., Nezamabadi, P.H., Sulaiman, M.N., Mustapha, N. (2014) A New Binary Particle Swarm Optimization for Feature Subset Selection with Support Vector Machine. In: Recent Advances on Soft Computing and Data Mining 2014 (pp. 47\u201357). Springer, Cham.","DOI":"10.1007\/978-3-319-07692-8_5"},{"issue":"3","key":"11584_CR30","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459\u2013471.","journal-title":"Journal of global optimization"},{"issue":"2","key":"11584_CR31","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/math7020135","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Wang, P., Zhang, J., Cui, Z., Cai, X., Zhang, W., & Chen, J. (2019). A novel bat algorithm with multiple strategies coupling for numerical optimization. Mathematics, 7(2), 135.","journal-title":"Mathematics"},{"issue":"4","key":"11584_CR32","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053\u20131073.","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"11584_CR33","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s12293-013-0128-0","volume":"6","author":"JC Bansal","year":"2014","unstructured":"Bansal, J. C., Sharma, H., Jadon, S. S., & Clerc, M. (2014). Spider monkey optimization algorithm for numerical optimization. Memetic Computing, 6(1), 31\u201347. https:\/\/doi.org\/10.1007\/s12293-013-0128-0","journal-title":"Memetic Computing"},{"key":"11584_CR34","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46\u201361. https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Advances in Engineering Software"},{"key":"11584_CR35","doi-asserted-by":"crossref","unstructured":"Xin-She, Y. (2010) Firefly algorithm, Levy flights and global optimization. In:\u00a0Research and development in intelligent systems XXVI, pp. 209\u2013218. Springer, London.","DOI":"10.1007\/978-1-84882-983-1_15"},{"key":"11584_CR36","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.asoc.2015.07.028","volume":"36","author":"H Shareef","year":"2015","unstructured":"Shareef, H. (2015). Ahmad Asrul Ibrahim, Ammar Hussein Mutlag: Lightning search algorithm. Applied Soft Computing, 36, 315\u2013333.","journal-title":"Applied Soft Computing"},{"issue":"13","key":"11584_CR37","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information sciences, 179(13), 2232\u20132248.","journal-title":"Information sciences"},{"key":"11584_CR38","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","volume":"222","author":"A Hatamlou","year":"2013","unstructured":"Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222, 175\u2013184.","journal-title":"Information sciences"},{"key":"11584_CR39","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.engappai.2016.04.004","volume":"54","author":"V Punnathanam","year":"2016","unstructured":"Punnathanam, V., & Kotecha, P. (2016). Yin-Yang-pair optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 54, 62\u201379.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"11584_CR40","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.compstruc.2012.07.010","volume":"11","author":"H Eskandar","year":"2012","unstructured":"Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm\u2014a novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 11, 151\u2013166.","journal-title":"Computers & Structures"},{"key":"11584_CR41","unstructured":"Xin-She, Y. (2009) Harmony search as a metaheuristic algorithm. In:\u00a0Music-inspired harmony search algorithm, Springer, Berlin, Heidelberg."},{"key":"11584_CR42","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.asoc.2017.11.043","volume":"64","author":"R Moghdani","year":"2018","unstructured":"Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64, 161\u2013185.","journal-title":"Applied Soft Computing"},{"issue":"3","key":"11584_CR43","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching\u2013learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303\u2013315.","journal-title":"Computer-Aided Design"},{"issue":"1","key":"11584_CR44","first-page":"19","volume":"7","author":"R Rao","year":"2016","unstructured":"Rao, R. (2016). Jaya\u2014a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19\u201334.","journal-title":"International Journal of Industrial Engineering Computations"},{"issue":"2","key":"11584_CR45","first-page":"141","volume":"10","author":"E Cant\u00fa-Paz","year":"1998","unstructured":"Cant\u00fa-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs paralleles, reseaux et systems repartis, 10(2), 141\u2013171.","journal-title":"Calculateurs paralleles, reseaux et systems repartis"},{"key":"11584_CR46","doi-asserted-by":"crossref","unstructured":"Gies, D., Rahmat-Samii, Y. (2003) Reconfigurable array design using parallel particle swarm optimization. In: IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC\/CNC\/URSI North American Radio Sci. Meeting (Cat. No. 03CH37450), Vol. 1, pp. 177\u2013180, IEEE.","DOI":"10.1109\/APS.2003.1217429"},{"issue":"13","key":"11584_CR47","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1002\/nme.1149","volume":"61","author":"JF Schutte","year":"2004","unstructured":"Schutte, J. F., Reinbolt, J. A., Fregly, B. J., Haftka, R. T., & George, A. D. (2004). Parallel global optimization with the particle swarm algorithm. International journal for numerical methods in engineering, 61(13), 2296\u20132315.","journal-title":"International journal for numerical methods in engineering"},{"issue":"3","key":"11584_CR48","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2514\/1.17873","volume":"3","author":"G Venter","year":"2006","unstructured":"Venter, G., & Jaroslaw, S. S. (2006). Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. Journal of Aerospace Computing, Information, and Communication, 3(3), 123\u2013137.","journal-title":"Journal of Aerospace Computing, Information, and Communication"},{"key":"11584_CR49","doi-asserted-by":"crossref","unstructured":"Narasimhan, H. (2009) Parallel artificial bee colony (PABC) algorithm. In: World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 306\u2013311. IEEE.","DOI":"10.1109\/NABIC.2009.5393726"},{"issue":"8","key":"11584_CR50","doi-asserted-by":"crossref","first-page":"5181","DOI":"10.1016\/j.asoc.2011.05.042","volume":"11","author":"M Pedemonte","year":"2011","unstructured":"Pedemonte, M., Nesmachnow, S., & Cancela, H. (2011). A survey on parallel ant colony optimization. Applied Soft Computing, 11(8), 5181\u20135197.","journal-title":"Applied Soft Computing"},{"key":"11584_CR51","doi-asserted-by":"crossref","unstructured":"Aljarah, I., Ludwig, S.A. (2012) Parallel particle swarm optimization clustering algorithm based on map-reduce methodology. In: Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 104\u2013111, IEEE.","DOI":"10.1109\/NaBIC.2012.6402247"},{"key":"11584_CR52","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.pnucene.2014.05.014","volume":"76","author":"F Khoshahval","year":"2014","unstructured":"Khoshahval, F., Zolfaghari, A., Minuchehr, H., & Abbasi, M. R. (2014). A new hybrid method for multi-objective fuel management optimization using parallel PSO-SA. Progress in Nuclear Energy, 76, 112\u2013121.","journal-title":"Progress in Nuclear Energy"},{"key":"11584_CR53","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compfluid.2014.05.020","volume":"110","author":"A Ouyang","year":"2015","unstructured":"Ouyang, A., Tang, Z., Zhou, X., Xu, Y., Pan, G., & Li, K. (2015). Parallel hybrid pso with cuda for ld heat conduction equation. Computers & Fluids, 110, 198\u2013210.","journal-title":"Computers & Fluids"},{"key":"11584_CR54","unstructured":"Esposito, A., Gomes, H.M. (2015) Evolutionary parallel and serial programming algorithm for structural optimization. In: recent advances in computer science and applications."},{"key":"11584_CR55","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.jpdc.2016.04.014","volume":"98","author":"R Skinderowicz","year":"2016","unstructured":"Skinderowicz, R. (2016). The GPU-based parallel ant colony system. Journal of Parallel and Distributed Computing, 98, 48\u201360.","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"11584_CR56","unstructured":"Thomas, S. Ant colony optimization\u2014public software. Accessed: 2014\u201306\u201318."},{"key":"11584_CR57","unstructured":"Wilt, N. (2013) The cuda handbook\u2014a comprehensive guide to gpu programming. In: Pearson Education."},{"key":"11584_CR58","unstructured":"Ortakci, Y. (2017) Parallel Particle Swarm Optimization in Data Clustering. In: International Journal of Soft Computing and Artificial Intelligence (IJSCAI)."},{"key":"11584_CR59","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.patrec.2018.10.015","volume":"116","author":"F Yan","year":"2018","unstructured":"Yan, F. (2018). Autonomous vehicle routing problem solution based on artificial potential field with parallel ant colony optimization (ACO) algorithm. Pattern Recognition Letters, 116, 195\u2013199.","journal-title":"Pattern Recognition Letters"},{"issue":"4","key":"11584_CR60","doi-asserted-by":"crossref","first-page":"2899","DOI":"10.1007\/s13369-018-03713-6","volume":"44","author":"S Lalwani","year":"2019","unstructured":"Lalwani, S., Sharma, H., Satapathy, S. C., Deep, K., & Bansal, J. C. (2019). A survey on parallel particle swarm optimization algorithms. Arabian Journal for Science and Engineering, 44(4), 2899\u20132923.","journal-title":"Arabian Journal for Science and Engineering"},{"key":"11584_CR61","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.future.2020.01.011","volume":"106","author":"R Skinderowicz","year":"2020","unstructured":"Skinderowicz, R. (2020). Implementing a GPU-based parallel MAX\u2013MIN Ant System. Future Generation Computer Systems, 106, 277\u2013295. https:\/\/doi.org\/10.1016\/j.future.2020.01.011","journal-title":"Future Generation Computer Systems"},{"key":"11584_CR62","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.jpdc.2017.12.002","volume":"113","author":"JM Cecilia","year":"2018","unstructured":"Cecilia, J. M., Llanes, A., Abell\u00e1n, J. L., Luna, J., Chang, L., & Hwu, W. W. (2018). High-throughput ant colony optimization on graphics processing units. Journal of Parallel Distribution Computing, 113, 261\u2013274. https:\/\/doi.org\/10.1016\/j.jpdc.2017.12.002","journal-title":"Journal of Parallel Distribution Computing"},{"key":"11584_CR63","doi-asserted-by":"crossref","unstructured":"Branke, J., Kau\u00dfler, T., Smidt, C., Schmeck, H. (2000) A multi-population approach to dynamic optimization problems. In:\u00a0Evolutionary design and manufacture\u00a0(pp. 299\u2013307), Springer, London.","DOI":"10.1007\/978-1-4471-0519-0_24"},{"key":"11584_CR64","doi-asserted-by":"crossref","unstructured":"Blackwell, T., Branke, J. (2004) Multi-swarm optimization in dynamic environments. In:\u00a0Workshops on Applications of Evolutionary Computation\u00a0(pp. 489\u2013500), Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-540-24653-4_50"},{"key":"11584_CR65","doi-asserted-by":"crossref","unstructured":"Zeng, G.P., Fan, H.L. (2014) Two-subpopulation particle swarm optimization based on pheromone diffusion. In:\u00a0Applied Mechanics and Materials\u00a0(Vol. 667, pp. 300\u2013308), Trans Tech Publications Ltd.","DOI":"10.4028\/www.scientific.net\/AMM.667.300"},{"key":"11584_CR66","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compeleceng.2017.01.025","volume":"58","author":"DV Medhane","year":"2017","unstructured":"Medhane, D. V., & Sangaiah, A. K. (2017). Search space-based multi-objective optimization evolutionary algorithm. Computers & Electrical Engineering, 58, 126\u2013143.","journal-title":"Computers & Electrical Engineering"},{"key":"11584_CR67","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.asoc.2018.02.042","volume":"67","author":"X Xia","year":"2018","unstructured":"Xia, X., Gui, L., & Zhan, Z. H. (2018). A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting. Applied Soft Computing, 67, 126\u2013140.","journal-title":"Applied Soft Computing"},{"issue":"1","key":"11584_CR68","first-page":"103","volume":"3","author":"KJ Binkley","year":"2008","unstructured":"Binkley, K. J., & Hagiwara, M. (2008). Balancing exploitation and exploration in particle swarm optimization: velocity-based reinitialization. Information and Media Technologies, 3(1), 103\u2013111.","journal-title":"Information and Media Technologies"},{"key":"11584_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2012.12.004","volume":"11","author":"BO Arani","year":"2013","unstructured":"Arani, B. O., Mirzabeygi, P., & Panahi, M. S. (2013). An improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration\u2013exploitation balance. Swarm and Evolutionary Computation, 11, 1\u201315.","journal-title":"Swarm and Evolutionary Computation"},{"key":"11584_CR70","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.swevo.2015.05.002","volume":"24","author":"N Lynn","year":"2015","unstructured":"Lynn, N., & Suganthan, P. N. (2015). Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 24, 11\u201324.","journal-title":"Swarm and Evolutionary Computation"},{"key":"11584_CR71","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.asoc.2017.04.050","volume":"59","author":"MJ Islam","year":"2017","unstructured":"Islam, M. J., Li, X., & Mei, Y. (2017). A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Applied Soft Computing, 59, 182\u2013196.","journal-title":"Applied Soft Computing"},{"issue":"18","key":"11584_CR72","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ifacol.2018.09.311","volume":"51","author":"L Hongru","year":"2018","unstructured":"Hongru, L., Jinxing, H., & Shouyong, J. (2018). A hybrid PSO based on dynamic clustering for global optimization. IFAC-PapersOnLine, 51(18), 269\u2013274.","journal-title":"IFAC-PapersOnLine"},{"key":"11584_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/5025672","volume":"2018","author":"X Lv","year":"2018","unstructured":"Lv, X., Wang, Y., Deng, J., Zhang, G., & Zhang, L. (2018). Improved particle swarm optimization algorithm based on last-eliminated principle and enhanced information sharing. Computational Intelligence and Neuroscience, 2018, 1\u201317. https:\/\/doi.org\/10.1155\/2018\/5025672","journal-title":"Computational Intelligence and Neuroscience"},{"key":"11584_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/6094685","volume":"2018","author":"S Chen","year":"2018","unstructured":"Chen, S., Liu, Y., Wei, L., & Guan, B. (2018). PS-FW: A hybrid algorithm based on particle swarm and fireworks for global optimization. Computational Intelligence and Neuroscience, 2018, 1\u201327. https:\/\/doi.org\/10.1155\/2018\/6094685","journal-title":"Computational Intelligence and Neuroscience"},{"issue":"1","key":"11584_CR75","doi-asserted-by":"publisher","first-page":"e123330","DOI":"10.1111\/exsy.12330","volume":"36","author":"E \u00c7omak","year":"2019","unstructured":"\u00c7omak, E. (2019). A particle swarm optimizer with modified velocity update and adaptive diversity regulation. Expert Systems, 36(1), e123330. https:\/\/doi.org\/10.1111\/exsy.12330","journal-title":"Expert Systems"},{"key":"11584_CR76","doi-asserted-by":"crossref","first-page":"105404","DOI":"10.1016\/j.knosys.2019.105404","volume":"193","author":"D Pelusi","year":"2020","unstructured":"Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2020). Improving exploration and exploitation via a hyperbolic gravitational search algorithm. Knowledge-Based Systems, 193, 105404.","journal-title":"Knowledge-Based Systems"},{"key":"11584_CR77","doi-asserted-by":"crossref","first-page":"106605","DOI":"10.1016\/j.compeleceng.2020.106605","volume":"84","author":"AM Pereira","year":"2020","unstructured":"Pereira, A. M., Vieira, T., & Costa, E. B. (2020). Balancing exploration and exploitation- An image-based approach to item retrieval with enhanced diversity. Computers & Electrical Engineering, 84, 106605.","journal-title":"Computers & Electrical Engineering"},{"key":"11584_CR78","doi-asserted-by":"crossref","unstructured":"Badoni, Rakesh P., Jayakrushna Sahoo, Shwetabh Srivastava, Mukesh Mann, D. K. Gupta, Swati Verma, Predrag S. Stanimirovi\u0107, Lev A. Kazakovtsev, and Darjan Karaba\u0161evi\u0107. (2023) \"An Exploration and Exploitation-Based Metaheuristic Approach for University Course Timetabling Problems.\" Axioms 12, no. 8: 720.","DOI":"10.3390\/axioms12080720"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11584-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-024-11584-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11584-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T12:23:11Z","timestamp":1728476591000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-024-11584-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":78,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["11584"],"URL":"https:\/\/doi.org\/10.1007\/s11277-024-11584-4","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"type":"print","value":"0929-6212"},{"type":"electronic","value":"1572-834X"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"20 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript entitled \u201cA Survey on Parallel Nature Inspired Algorithms\u201d.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}