{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:50:24Z","timestamp":1785336624736,"version":"3.55.0"},"reference-count":108,"publisher":"Springer Science and Business Media LLC","issue":"13-14","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012330","name":"Okayama University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012330","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.<\/jats:p>","DOI":"10.1007\/s10489-024-05502-1","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T06:02:28Z","timestamp":1717394548000},"page":"7229-7277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients"],"prefix":"10.1007","volume":"54","author":[{"given":"Keigo","family":"Watanabe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongshi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"5502_CR1","doi-asserted-by":"publisher","unstructured":"Abd\u00a0Latiff I, Tokhi MO (2009) Fast convergence strategy for particle swarm optimization using spread factor. In: Proceeding of 2009 IEEE congress on evolutionary computation (CEC 2009), pp 2693\u20132700, https:\/\/doi.org\/10.1109\/CEC.2009.4983280","DOI":"10.1109\/CEC.2009.4983280"},{"key":"5502_CR2","doi-asserted-by":"publisher","first-page":"8565","DOI":"10.3390\/en15228565","volume":"15","author":"MS Alvarez-Alvarado","year":"2022","unstructured":"Alvarez-Alvarado MS, Rengifo J, Gallegos-N\u00fa\u00f1ez RM et al (2022) Particle swarm optimization for optimal frequency response with high penetration of photovoltaic and wind generation. Energies 15:8565. https:\/\/doi.org\/10.3390\/en15228565","journal-title":"Energies"},{"key":"5502_CR3","doi-asserted-by":"publisher","unstructured":"Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1\u201312. https:\/\/doi.org\/10.1016\/j.compstruc.2016.03.001","DOI":"10.1016\/j.compstruc.2016.03.001"},{"key":"5502_CR4","unstructured":"Awad NH, Ali MZ, Liang JJ, et\u00a0al (2016) Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. Tech. rep., Nanyang Technological University, Jordan University of Science and Technology, Zhengzhou University, https:\/\/github.com\/P-N-Suganthan\/CEC2017-BoundContrained"},{"issue":"2","key":"5502_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.9781\/ijimai.2023.01.004","volume":"8","author":"HQ Awla","year":"2023","unstructured":"Awla HQ, Kareem SW, Mohammed AS (2023) A comparative evaluation of bayesian networks structure learning using falcon optimization algorithm. Int J Interact Multimed Artif Intell 8(2):81\u201387. https:\/\/doi.org\/10.9781\/ijimai.2023.01.004","journal-title":"Int J Interact Multimed Artif Intell"},{"issue":"12","key":"5502_CR6","doi-asserted-by":"publisher","first-page":"478","DOI":"10.17577\/IJERTV8IS120252","volume":"8","author":"M Basavanna","year":"2019","unstructured":"Basavanna M, Shivakumar M (2019) An overview of path planning and obstacle avoidance algorithms in mobile robots. Int J Eng Technol 8(12):478\u2013482. https:\/\/doi.org\/10.17577\/IJERTV8IS120252","journal-title":"Int J Eng Technol"},{"key":"5502_CR7","doi-asserted-by":"publisher","unstructured":"Ben Khoud K, Bouall\u00e8gue S, Ayadi M (2018) Design and co-simulation of a fuzzy gain-scheduled pid controller based on particle swarm optimization algorithms for a quad tilt wing unmanned aerial vehicle. Trans Inst Meas Control 40(14):3933\u20133952. https:\/\/doi.org\/10.1177\/0142331217740947","DOI":"10.1177\/0142331217740947"},{"issue":"5","key":"5502_CR8","doi-asserted-by":"publisher","first-page":"785","DOI":"10.3390\/math8050785","volume":"8","author":"F Caraffini","year":"2020","unstructured":"Caraffini F, Iacca G (2020) The sos platform: designing, tuning and statistically benchmarking optimization algorithms. Mathematics 8(5):785. https:\/\/doi.org\/10.3390\/math8050785","journal-title":"Mathematics"},{"key":"5502_CR9","doi-asserted-by":"publisher","DOI":"10.9781\/ijimai.2024.01.002","author":"S Carstensen","year":"2024","unstructured":"Carstensen S, Lin JCW (2024) Tku-pso: an efficient particle swarm optimization model for top-k high-utility itemset mining. Int J Interact Multimed Artif Intell. https:\/\/doi.org\/10.9781\/ijimai.2024.01.002","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"5502_CR10","doi-asserted-by":"publisher","unstructured":"Chai WS, bin Romli MIF, Yaakob SB et al (2022) Regenerative braking optimization using particle swarm algorithm for electric vehicle. J Adv Comput Intell Intell Inform 26(6):1022\u20131030. https:\/\/doi.org\/10.20965\/jaciii.2022.p1022","DOI":"10.20965\/jaciii.2022.p1022"},{"key":"5502_CR11","doi-asserted-by":"publisher","unstructured":"Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859\u2013871. https:\/\/doi.org\/10.1016\/j.cor.2004.08.012","DOI":"10.1016\/j.cor.2004.08.012"},{"key":"5502_CR12","unstructured":"Chen G, Jia J (2006) Han Q (2006 (in Chinese)) Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm. J Xi\u2019an Jiaotong Univ 40(1):53\u201356. 0253-987X, 01-0053-04"},{"issue":"4","key":"5502_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.16772\/j.cnki.1673-1409.2007.04.047","volume":"4","author":"S Chen","year":"2007","unstructured":"Chen S, Cai G, Guo W et al (2007) (in Chinese)) Study on the nonlinear strategy of acceleration coefficient in particle swarm optimization (pso) algorithm. J of Yangtze University Sci & Eng (Nat Sci Ed) 4(4):1\u20134. https:\/\/doi.org\/10.16772\/j.cnki.1673-1409.2007.04.047","journal-title":"J of Yangtze University Sci & Eng (Nat Sci Ed)"},{"key":"5502_CR14","doi-asserted-by":"publisher","unstructured":"Chong X (2021) Hybrid pso-svm for financial early-warning model of small and medium-sized enterprises. In: Proceedings of the 6th International Conference on Financial Innovation and Economic Development (ICFIED 2021), pp 107\u2013114, https:\/\/doi.org\/10.2991\/aebmr.k.210319.020","DOI":"10.2991\/aebmr.k.210319.020"},{"key":"5502_CR15","doi-asserted-by":"publisher","unstructured":"Clerc M, Kennedy J (2002) The particle swarm\u2013explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58\u201373. https:\/\/doi.org\/10.1109\/4235.985692","DOI":"10.1109\/4235.985692"},{"key":"5502_CR16","doi-asserted-by":"publisher","unstructured":"Derrouaoui SH, Bouzid Y, Guiatni M (2021) Pso based optimal gain scheduling backstepping flight controller design for a transformable quadrotor. J Intell Robot Syst 102(3):1\u201325. https:\/\/doi.org\/10.1007\/s10846-021-01422-1","DOI":"10.1007\/s10846-021-01422-1"},{"key":"5502_CR17","doi-asserted-by":"publisher","unstructured":"Du Y, Xu F (2020) A hybrid multi-step probability selection particle swarm optimization with dynamic chaotic inertial weight and acceleration coefficients for numerical function optimization. Symmetry 12(922):1\u201325. https:\/\/doi.org\/10.3390\/sym12060922","DOI":"10.3390\/sym12060922"},{"key":"5502_CR18","doi-asserted-by":"publisher","unstructured":"Duan H, Qiao P (2014) Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intell Comput Cybern 7(1):24\u201337. https:\/\/doi.org\/10.1108\/IJICC-02-2014-0005","DOI":"10.1108\/IJICC-02-2014-0005"},{"key":"5502_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/9780470512517","volume-title":"Computational Intelligence: An Introduction","author":"AP Engelbrecht","year":"2007","unstructured":"Engelbrecht AP (2007) Computational Intelligence: An Introduction, 2nd edn. John Wiley and Sons, West Sussex, UK","edition":"2"},{"key":"5502_CR20","doi-asserted-by":"publisher","unstructured":"Faria J, Marques C, Pombo J et al (2023) Optimal sizing of renewable energy communities: a multiple swarms multi-objective particle swarm optimization approach. Energies 16:7227. https:\/\/doi.org\/10.3390\/en16217227","DOI":"10.3390\/en16217227"},{"key":"5502_CR21","doi-asserted-by":"publisher","unstructured":"Fernandes Junior FE, Yen GG (2019) Particle swarm optimization of deep neural networks architectures for image classification. Swarm Evol Comput 49:62\u201374. https:\/\/doi.org\/10.1016\/j.swevo.2019.05.010","DOI":"10.1016\/j.swevo.2019.05.010"},{"key":"5502_CR22","doi-asserted-by":"crossref","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701. http:\/\/www.jstor.org\/stable\/2279372","DOI":"10.1080\/01621459.1937.10503522"},{"key":"5502_CR23","doi-asserted-by":"publisher","unstructured":"Fu Y, Ding M, Zhou C et\u00a0al (2009) Path planning for uav based on quantum-behaved particle swarm optimization. In: Proceeding of medical imaging, parallel processing of images, and optimization techniques (MIPPR 2009), https:\/\/doi.org\/10.1117\/12.832476","DOI":"10.1117\/12.832476"},{"key":"5502_CR24","doi-asserted-by":"publisher","unstructured":"Fu Y, Ding M, Zhou C (2012) Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for uav. IEEE Trans Syst Man Cybern Part A Syst Hum 42(2):511\u2013526. https:\/\/doi.org\/10.1109\/TSMCA.2011.2159586","DOI":"10.1109\/TSMCA.2011.2159586"},{"key":"5502_CR25","doi-asserted-by":"publisher","unstructured":"Gazi V (2012) Stochastic stability analysis of the particle dynamics in the pso algorithm. In: Proceeding of 2012 IEEE int. symp. on intelligent control (ISIC), Part of 2012 IEEE multi-conference on systems and control, Dubrovnik, Croatia, pp 708\u2013713, https:\/\/doi.org\/10.1109\/ISIC.2012.6398264","DOI":"10.1109\/ISIC.2012.6398264"},{"key":"5502_CR26","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s40747-020-00252-2","volume":"7","author":"N Geng","year":"2021","unstructured":"Geng N, Chen Z, Nguyen QA et al (2021) Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints. Complex Intell Syst 7:873\u2013890. https:\/\/doi.org\/10.1007\/s40747-020-00252-2","journal-title":"Complex Intell Syst"},{"key":"5502_CR27","doi-asserted-by":"publisher","unstructured":"Ghasemi M, Akbari E, Rahimnejad A et al (2019) Phasor particle swarm optimization: a simple and efficient variant of pso. Soft Comput 23:9701\u20139718. https:\/\/doi.org\/10.1007\/s00500-018-3536-8","DOI":"10.1007\/s00500-018-3536-8"},{"key":"5502_CR28","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1155\/2022\/2015538","volume":"2022","author":"X He","year":"2022","unstructured":"He X, Chen Y, Hu K et al (2022) Application of pso-optimized twin support vector machine in medium and long-term load forecasting under the background of new normal economy. Adv Multimed 2022:9. https:\/\/doi.org\/10.1155\/2022\/2015538","journal-title":"Adv Multimed"},{"issue":"5","key":"5502_CR29","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1109\/TEVC.2012.2211025","volume":"17","author":"M Hu","year":"2013","unstructured":"Hu M, Wu T, Weir JD (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evol Comput 17(5):705\u2013720. https:\/\/doi.org\/10.1109\/TEVC.2012.2211025","journal-title":"IEEE Trans Evol Comput"},{"key":"5502_CR30","doi-asserted-by":"publisher","first-page":"10088","DOI":"10.3390\/app131810088","volume":"13","author":"S Hu","year":"2023","unstructured":"Hu S, Li K (2023) Bayesian network demand-forecasting model based on modified particle swarm optimization. Appl Sci 13:10088. https:\/\/doi.org\/10.3390\/app131810088","journal-title":"Appl Sci"},{"key":"5502_CR31","doi-asserted-by":"publisher","first-page":"16409","DOI":"10.3390\/su142416409","volume":"14","author":"Y Huang","year":"2022","unstructured":"Huang Y, Wang X, Chen H (2022) Location selection for regional logistics center based on particle swarm optimization. Sustainability 14:16409. https:\/\/doi.org\/10.3390\/su142416409","journal-title":"Sustainability"},{"key":"5502_CR32","doi-asserted-by":"publisher","unstructured":"Hung CW, Mao WL, Huang HY (2019) Modified pso algorithm on recurrent fuzzy neural network for system identification. Intell Autom Soft Comput 25(2):329\u2013341. https:\/\/doi.org\/10.31209\/2019.100000093","DOI":"10.31209\/2019.100000093"},{"key":"5502_CR33","doi-asserted-by":"publisher","unstructured":"Huynh NT, Nguyen TVT, Tam NT et\u00a0al (2021) Optimizing magnification ratio for the flexible hinge displacement amplifier mechanism design. In: Long B, Kim Y, Ishizaki K, et\u00a0al (eds) Proceedings of the 2nd annual international conference on Material, Machines and Methods for Sustainable development (MMMS2020), p 102, https:\/\/doi.org\/10.1007\/978-3-030-69610-8_102","DOI":"10.1007\/978-3-030-69610-8_102"},{"key":"5502_CR34","unstructured":"Innocente MS, Sienz J (2010) Coefficients\u2019 settings in particle swarm optimization: insight and guidelines. In: Proceeding of IX argentinean congress on computational mechanics, II South American Congress on Computational Mechanics, and XXXI Iberian-Latin-American Congress on Computational Methods in Engineering, Buenos Aires, Argentina, pp 9253\u20139269, https:\/\/cimec.org.ar\/ojs\/index.php\/mc\/article\/view\/3666"},{"key":"5502_CR35","doi-asserted-by":"publisher","unstructured":"Jiang M, Luo Y, Yang S (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf Process Lett 102(1):8\u201316. https:\/\/doi.org\/10.1016\/j.ipl.2006.10.005","DOI":"10.1016\/j.ipl.2006.10.005"},{"key":"5502_CR36","doi-asserted-by":"publisher","unstructured":"Jiang S, Jiang J, Zheng C et\u00a0al (2019) An improved pso algorithm with migration behavior and asynchronous varying acceleration coefficient. In: Proceeding of 15th int. conference (ICIC 2019), Nanchang, China, pp 651\u2013659, https:\/\/doi.org\/10.1007\/978-3-030-26766-7_59","DOI":"10.1007\/978-3-030-26766-7_59"},{"key":"5502_CR37","doi-asserted-by":"publisher","unstructured":"Kadirkamanathan V, Selvarajah K, Fleming PJ (2006) Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Trans Evol Comput 10(3):245\u2013255. https:\/\/doi.org\/10.1109\/TEVC.2005.857077","DOI":"10.1109\/TEVC.2005.857077"},{"key":"5502_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/7935346","volume":"2022","author":"C Kaushal","year":"2022","unstructured":"Kaushal C, Islam MK, Althubiti SA et al (2022) A framework for interactive medical image segmentation using optimized swarm intelligence with convolutional neural networks. Comput Intell Neurosci 2022:1\u201321. https:\/\/doi.org\/10.1155\/2022\/7935346","journal-title":"Comput Intell Neurosci"},{"key":"5502_CR39","doi-asserted-by":"publisher","unstructured":"Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceeding of the IEEE int. conf. on evolutionary computation, pp 303\u2013308, https:\/\/doi.org\/10.1109\/ICEC.1997.592326","DOI":"10.1109\/ICEC.1997.592326"},{"key":"5502_CR40","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of int. conference neural network (ICNN), pp 1942\u20131948, https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"5502_CR41","doi-asserted-by":"publisher","unstructured":"Kerboua A, Boukli-Hacene F, Mourad KA (2020) Particle swarm optimization for micro-grid power management and load scheduling. Int J Energy Econ Pol 10(2):71\u201380. https:\/\/doi.org\/10.32479\/ijeep.8568","DOI":"10.32479\/ijeep.8568"},{"issue":"13","key":"5502_CR42","doi-asserted-by":"publisher","first-page":"5190","DOI":"10.1080\/01431161.2021.1910369","volume":"42","author":"N Kothandaraman","year":"2021","unstructured":"Kothandaraman N, Kaliaperumal V (2021) Combined particle swarm optimization and modified bilinear model (pso-mbm) algorithm for nonlinearity detection and spectral unmixing of satellite imageries. Int J Remote Sens 42(13):5190\u20135209. https:\/\/doi.org\/10.1080\/01431161.2021.1910369","journal-title":"Int J Remote Sens"},{"key":"5502_CR43","doi-asserted-by":"publisher","first-page":"7673","DOI":"10.3390\/app13137673","volume":"13","author":"L Kumar","year":"2023","unstructured":"Kumar L, Singh KU, Kumar I et al (2023) Robust medical image watermarking scheme using pso, lwt, and hessenberg decomposition. Appl Sci 13:7673. https:\/\/doi.org\/10.3390\/app13137673","journal-title":"Appl Sci"},{"key":"5502_CR44","unstructured":"Lee KH (2005) First Course on Fuzzy Theory and Applications. Springer, Heidelberg, https:\/\/link.springer.com\/book\/10.1007\/3-540-32366-X"},{"key":"5502_CR45","doi-asserted-by":"publisher","unstructured":"Lei K, Qiu Y, He Y (2006) A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. In: Proceeding of IEEE 2006 1st int. symp. on systems and control in aerospace and astronautics, Harbin, China, pp 977\u2013980, https:\/\/doi.org\/10.1109\/ISSCAA.2006.1627487","DOI":"10.1109\/ISSCAA.2006.1627487"},{"issue":"4","key":"5502_CR46","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.gloei.2020.10.001","volume":"3","author":"Z Li","year":"2020","unstructured":"Li Z, Han X, Yang M et al (2020) Multi-stage power source and grid coordination planning method considering grid uniformity. Global Energy Interconnection 3(4):303\u2013312. https:\/\/doi.org\/10.1016\/j.gloei.2020.10.001","journal-title":"Global Energy Interconnection"},{"key":"5502_CR47","unstructured":"Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. In: Technical report 201311, computational intelligence laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore, https:\/\/www.researchgate.net\/publication\/271646935"},{"key":"5502_CR48","doi-asserted-by":"publisher","first-page":"40963","DOI":"10.1109\/ACCESS.2021.3065532","volume":"9","author":"K Liu","year":"2021","unstructured":"Liu K, Cui Y, Ren J et al (2021) An improved particle swarm optimization algorithm for bayesian network structure learning via local information constraint. IEEE Access 9:40963\u201340971. https:\/\/doi.org\/10.1109\/ACCESS.2021.3065532","journal-title":"IEEE Access"},{"key":"5502_CR49","doi-asserted-by":"publisher","unstructured":"Liu X, Hou G, Yang L (2023) Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm. Front Earth Sci 11. https:\/\/doi.org\/10.3389\/feart.2023.1134523","DOI":"10.3389\/feart.2023.1134523"},{"key":"5502_CR50","doi-asserted-by":"publisher","unstructured":"Lu J, Hu H, Bai Y (2014) Radial basis function neural network based on an improved exponential decreasing inertia weight-particle swarm optimization algorithm for aqi prediction. Abstr Appl Anal 2014(SI11):1\u20139. https:\/\/doi.org\/10.1155\/2014\/178313","DOI":"10.1155\/2014\/178313"},{"key":"5502_CR51","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1155\/2021\/6385713","volume":"2021","author":"H Marouani","year":"2021","unstructured":"Marouani H (2021) Optimization for the redundancy allocation problem of reliability using an improved particle swarm optimization algorithm. J Optim 2021:9. https:\/\/doi.org\/10.1155\/2021\/6385713","journal-title":"J Optim"},{"key":"5502_CR52","doi-asserted-by":"publisher","unstructured":"Melo AG, Andrade FAA, Guedes IP et al (2022) Fuzzy gain-scheduling pid for uav position and altitude controllers. Sensors 22(2173):1\u201321. https:\/\/doi.org\/10.3390\/s22062173","DOI":"10.3390\/s22062173"},{"key":"5502_CR53","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.3390\/en15062211","volume":"15","author":"C Menos-Aikateriniadis","year":"2022","unstructured":"Menos-Aikateriniadis C, Lamprinos I, Georgilakis PS (2022) Particle swarm optimization in residential demand-side management: a review on scheduling and control algorithms for demand response provision. Energies 15:2211. https:\/\/doi.org\/10.3390\/en15062211","journal-title":"Energies"},{"key":"5502_CR54","doi-asserted-by":"publisher","first-page":"724","DOI":"10.3390\/machines11070724","volume":"11","author":"D Mourtzis","year":"2023","unstructured":"Mourtzis D, Angelopoulos J (2023) Reactive power optimization based on the application of an improved particle swarm optimization algorithm. Machines 11:724. https:\/\/doi.org\/10.3390\/machines11070724","journal-title":"Machines"},{"key":"5502_CR55","doi-asserted-by":"publisher","first-page":"422","DOI":"10.3390\/en17020422","volume":"17","author":"MA Mquqwana","year":"2024","unstructured":"Mquqwana MA, Krishnamurthy S (2024) Particle swarm optimization for an optimal hybrid renewable energy microgrid system under uncertainty. Energies 17:422. https:\/\/doi.org\/10.3390\/en17020422","journal-title":"Energies"},{"key":"5502_CR56","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1007\/s11831-022-09849-x","volume":"30","author":"J Nayak","year":"2023","unstructured":"Nayak J, Swapnarekha H, Naik B et al (2023) 25 years of particle swarm optimization: flourishing voyage of two decades. Arch Comput Methods Eng 30:1663\u20131725. https:\/\/doi.org\/10.1007\/s11831-022-09849-x","journal-title":"Arch Comput Methods Eng"},{"key":"5502_CR57","doi-asserted-by":"publisher","first-page":"74340","DOI":"10.1007\/s11356-023-27516-x","volume":"30","author":"HD Nguyen","year":"2023","unstructured":"Nguyen HD, Van CP, Nguyen TG et al (2023) Soil salinity prediction using hybrid machine learning and remote sensing in ben tre province on vietnam\u2019s mekong river delta. Environ Sci Pollut Res 30:74340\u201374357. https:\/\/doi.org\/10.1007\/s11356-023-27516-x","journal-title":"Environ Sci Pollut Res"},{"key":"5502_CR58","doi-asserted-by":"publisher","unstructured":"Phung MD, Ha QP (2021) Safety-enhanced uav path planning with spherical vector-based particle swarm optimization. Appl Soft Comput 107(107376):1\u201315. https:\/\/doi.org\/10.1016\/j.asoc.2021.107376","DOI":"10.1016\/j.asoc.2021.107376"},{"key":"5502_CR59","doi-asserted-by":"publisher","unstructured":"Poli R (2009) Mean and variance of the sampling distribution of particle swarm optimizers during stagnation. IEEE Trans Evol Comput 13(4):712\u2013721. https:\/\/doi.org\/10.1109\/TEVC.2008.2011744","DOI":"10.1109\/TEVC.2008.2011744"},{"key":"5502_CR60","doi-asserted-by":"publisher","unstructured":"Poli R, Broomhead D (2007) Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnation. In: Proceeding IEEE int. conf. on genetic and evolutionary computation conference (GECCO\u201907), London, England, pp 134\u2013141, https:\/\/doi.org\/10.1145\/1276958.1276977","DOI":"10.1145\/1276958.1276977"},{"issue":"12","key":"5502_CR61","doi-asserted-by":"publisher","first-page":"3915","DOI":"10.1007\/s12555-021-0371-y","volume":"20","author":"BK Priya","year":"2022","unstructured":"Priya BK, Reddy DA, Soliman WG et al (2022) Hybrid stepper motor: model, open-loop test, traditional pi, optimized pi, and optimized gain scheduled pi controllers. Int J Control Autom Syst 20(12):3915\u2013922. https:\/\/doi.org\/10.1007\/s12555-021-0371-y","journal-title":"Int J Control Autom Syst"},{"key":"5502_CR62","doi-asserted-by":"publisher","first-page":"4780","DOI":"10.3390\/app11114780","volume":"11","author":"MS Qamar","year":"2021","unstructured":"Qamar MS, Ali F, Armghan A et al (2021) Improvement of traveling salesman problem solution using hybrid algorithm based on best-worst ant system and particle swarm optimization. Appl Sci 11:4780. https:\/\/doi.org\/10.3390\/app11114780","journal-title":"Appl Sci"},{"key":"5502_CR63","doi-asserted-by":"publisher","first-page":"6444","DOI":"10.3390\/app13116444","volume":"13","author":"S Qu","year":"2023","unstructured":"Qu S, He T, Zhu G (2023) Model-assisted online optimization of gain-scheduled pid control using nsga-ii iterative genetic algorithm. Appl Sci 13:6444. https:\/\/doi.org\/10.3390\/app13116444","journal-title":"Appl Sci"},{"key":"5502_CR64","doi-asserted-by":"publisher","unstructured":"Rajania DK, Kumarc V (2020) Impact of controlling parameters on the performance of mopso algorithm. In: International Conference on Computational Intelligence and Data Science (ICCIDS 2019), pp 2132\u20132139, https:\/\/doi.org\/10.1016\/j.procs.2020.03.272","DOI":"10.1016\/j.procs.2020.03.272"},{"key":"5502_CR65","doi-asserted-by":"publisher","unstructured":"Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240\u2013255. https:\/\/doi.org\/10.1109\/TEVC.2004.826071","DOI":"10.1109\/TEVC.2004.826071"},{"key":"5502_CR66","doi-asserted-by":"publisher","first-page":"556","DOI":"10.3390\/info14100556","volume":"14","author":"A del Rio","year":"2023","unstructured":"del Rio A, Barambones O, Uralde J et al (2023) Particle swarm optimization-based control for maximum power point tracking implemented in a real time photovoltaic system. Information 14:556. https:\/\/doi.org\/10.3390\/info14100556","journal-title":"Information"},{"key":"5502_CR67","doi-asserted-by":"publisher","unstructured":"Roy C, Das DK (2021) A hybrid genetic algorithm (ga)\u2014particle swarm optimization (pso) algorithm for demand side management in smart grid considering wind power for cost optimization. S$$\\bar{\\text{a}}$$dhan$$\\bar{\\text{ a }}$$ 46. https:\/\/doi.org\/10.1007\/s12046-021-01626-z","DOI":"10.1007\/s12046-021-01626-z"},{"key":"5502_CR68","doi-asserted-by":"publisher","first-page":"923","DOI":"10.3390\/app14020923","volume":"14","author":"S Saifullah","year":"2024","unstructured":"Saifullah S, Drezewski R (2024) Advanced medical image segmentation enhancement: a particle-swarm-optimization-based histogram equalization approach. Appl Sci 14:923. https:\/\/doi.org\/10.3390\/app14020923","journal-title":"Appl Sci"},{"key":"5502_CR69","doi-asserted-by":"publisher","unstructured":"Sedghizadeh S, Beheshti S (2018) Particle swarm optimization based fuzzy gain scheduled subspace predictive control. Eng Appl Artif Intell 67:331\u2013344. https:\/\/doi.org\/10.1016\/j.engappai.2017.10.009","DOI":"10.1016\/j.engappai.2017.10.009"},{"key":"5502_CR70","doi-asserted-by":"publisher","unstructured":"Shami TM, El-Saleh AA, Alswaitti M et al (2022) Particle swarm optimization: a comprehensive survey. IEEE Access 10:10031\u201310061. https:\/\/doi.org\/10.1109\/ACCESS.2022.3142859","DOI":"10.1109\/ACCESS.2022.3142859"},{"issue":"4","key":"5502_CR71","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.1007\/s41870-021-00856-y","volume":"14","author":"MS Sheela","year":"2022","unstructured":"Sheela MS, Arun CA (2022) Hybrid pso-svm algorithm for covid-19 screening and quantification. Int J Inf Technol 14(4):2049\u20132056. https:\/\/doi.org\/10.1007\/s41870-021-00856-y","journal-title":"Int J Inf Technol"},{"key":"5502_CR72","doi-asserted-by":"publisher","unstructured":"Shi K, Yuan X, Liu L (2018) Model predictive controller-based multi-model control system for longitudinal stability of distributed drive electric vehicle. ISA Trans 72:44\u201355. https:\/\/doi.org\/10.1016\/j.isatra.2017.10.013","DOI":"10.1016\/j.isatra.2017.10.013"},{"key":"5502_CR73","doi-asserted-by":"publisher","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceeding of IEEE international conference on evolutionary computation, Anchorage, pp 69\u201373, https:\/\/doi.org\/10.1109\/ICEC.1998.699146","DOI":"10.1109\/ICEC.1998.699146"},{"key":"5502_CR74","doi-asserted-by":"publisher","unstructured":"Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceeding of the 1999 Congress on Evolutionary Computation (CEC99), pp 1945\u20131950, https:\/\/doi.org\/10.1109\/CEC.1999.785511","DOI":"10.1109\/CEC.1999.785511"},{"key":"5502_CR75","doi-asserted-by":"publisher","unstructured":"Shu J, Li J (2009) An improved self-adaptive particle swarm optimization algorithm with simulated annealing. In: Proceeding of the 3rd international conference on intelligent information technology application (IITA \u201909), Nanchang, China, pp 396\u2013399, https:\/\/doi.org\/10.1109\/IITA.2009.476","DOI":"10.1109\/IITA.2009.476"},{"key":"5502_CR76","doi-asserted-by":"publisher","first-page":"32249","DOI":"10.1109\/ACCESS.2021.3060464","volume":"9","author":"M Song","year":"2021","unstructured":"Song M, Liu S, Li W et al (2021) A continuous space location model and a particle swarm optimization-based heuristic algorithm for maximizing the allocation of ocean-moored buoys. IEEE Access 9:32249\u201332262. https:\/\/doi.org\/10.1109\/ACCESS.2021.3060464","journal-title":"IEEE Access"},{"key":"5502_CR77","doi-asserted-by":"publisher","first-page":"012010","DOI":"10.1088\/1742-6596\/2189\/1\/012010","volume":"2189","author":"X Song","year":"2022","unstructured":"Song X, Wang C (2022) Hyperspectral remote sensing image classification based on spectral-spatial feature fusion and pso algorithm. J Phys Conf Series 2189:012010. https:\/\/doi.org\/10.1088\/1742-6596\/2189\/1\/012010","journal-title":"J Phys Conf Series"},{"key":"5502_CR78","doi-asserted-by":"publisher","first-page":"161008","DOI":"10.1109\/ACCESS.2019.2951195","volume":"7","author":"T Su","year":"2019","unstructured":"Su T, Xu H, Zhou X (2019) Particle swarm optimization-based association rule mining in big data environment. IEEE Access 7:161008\u2013161016. https:\/\/doi.org\/10.1109\/ACCESS.2019.2951195","journal-title":"IEEE Access"},{"key":"5502_CR79","doi-asserted-by":"publisher","unstructured":"Sugeno M (1993) Fuzzy measures and fuzzy integrals\u2014 a survey. In: Dubois D, Prade H, Yager RR (eds) Readings in fuzzy sets for intelligent systems. Morgan Kaufmann, p 251\u2013257, https:\/\/doi.org\/10.1016\/B978-1-4832-1450-4.50027-4","DOI":"10.1016\/B978-1-4832-1450-4.50027-4"},{"key":"5502_CR80","doi-asserted-by":"crossref","unstructured":"Susanto ADNW, Suparwito H (2023) Svm-pso algorithm for tweet sentiment analysis #besoksenin. Indonesian Journal of Information Systems 6(1):36\u201347 https:\/\/ojs.uajy.ac.id\/index.php\/IJIS\/issue\/view\/437","DOI":"10.24002\/ijis.v6i1.7551"},{"key":"5502_CR81","doi-asserted-by":"publisher","unstructured":"Tosoni D, Galli C, Hanne T et\u00a0al (2022) Benchmarking metaheuristic optimization algorithms on travelling salesman problems. In: Proceedings of the international conference on e-society, e-learning and e-technologies (ICSLT \u201922), pp 20\u201325, https:\/\/doi.org\/10.1145\/3545922.3545926","DOI":"10.1145\/3545922.3545926"},{"key":"5502_CR82","doi-asserted-by":"publisher","unstructured":"Tran VT, Le MH, Vo MT et al (2023) Optimization design for die-sinking edm process parameters employing effective intelligent method. Cogent Eng 10(2). https:\/\/doi.org\/10.1080\/23311916.2023.2264060","DOI":"10.1080\/23311916.2023.2264060"},{"key":"5502_CR83","doi-asserted-by":"publisher","unstructured":"Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85:317\u2013325. https:\/\/doi.org\/10.1016\/S0020-0190(02)00447-7","DOI":"10.1016\/S0020-0190(02)00447-7"},{"issue":"855","key":"5502_CR84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s22030855","volume":"22","author":"P Trojovsky","year":"2022","unstructured":"Trojovsky P, Dehghani M (2022) Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors 22(855):1\u201334. https:\/\/doi.org\/10.3390\/s22030855","journal-title":"Sensors"},{"key":"5502_CR85","doi-asserted-by":"publisher","unstructured":"Waluyo A, Jatnika H, Permatasari MRS, et\u00a0al (2020) Data mining optimization uses c4.5 classification and particle swarm optimization (pso) in the location selection of student boardinghouses. In: IOP Conference Series: Materials Science and Engineering, https:\/\/doi.org\/10.1088\/1757-899X\/874\/1\/012024","DOI":"10.1088\/1757-899X\/874\/1\/012024"},{"key":"5502_CR86","doi-asserted-by":"publisher","unstructured":"Wang G, Liu Z (2012) An analysis of nonlinear acceleration coefficients adjustment for pso. In: Proceeding of 4th international conference on Artificial Intelligence and Computational Intelligence (AICI 2012), Chengdu, China, pp 698\u2013705, https:\/\/doi.org\/10.1007\/978-3-642-33478-8_86","DOI":"10.1007\/978-3-642-33478-8_86"},{"key":"5502_CR87","doi-asserted-by":"publisher","first-page":"6190","DOI":"10.3390\/rs14246190","volume":"14","author":"L Wang","year":"2022","unstructured":"Wang L, Guo N, Yue P et al (2022) Regulation of evapotranspiration in different precipitation zones and its application in high-temperature and drought monitoring. Remote Sensing 14:6190. https:\/\/doi.org\/10.3390\/rs14246190","journal-title":"Remote Sensing"},{"issue":"6","key":"5502_CR88","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80\u201383. https:\/\/doi.org\/10.2307\/3001968","journal-title":"Biometrics Bull"},{"key":"5502_CR89","doi-asserted-by":"publisher","unstructured":"Wu Y, Wu C, Wang L et\u00a0al (2021) Radar target tracking algorithm based on new particle swarm optimization particle filter. In: Proceeding of the 10th International Conference on Networks, Communication and Computing (ICNCC 2021), Beijing, China, pp 91\u201396, https:\/\/doi.org\/10.1145\/3510513.3510528","DOI":"10.1145\/3510513.3510528"},{"key":"5502_CR90","doi-asserted-by":"publisher","unstructured":"Wu Z, Zhou J (2007) A self-adaptive particle swarm optimization algorithm with individual coefficients adjustment. In: Proceeding of 2007 international conference on Computational Intelligence and Security (CIS 2007), Harbin, China, pp 133\u2013136, https:\/\/doi.org\/10.1109\/CIS.2007.95","DOI":"10.1109\/CIS.2007.95"},{"key":"5502_CR91","doi-asserted-by":"publisher","unstructured":"Xiao Y, Zhang L (2023) Smart grid energy storage capacity planning and scheduling optimization through pso-gru and multihead-attention. Front Energy Res 11. https:\/\/doi.org\/10.3389\/fenrg.2023.1254371","DOI":"10.3389\/fenrg.2023.1254371"},{"key":"5502_CR92","doi-asserted-by":"publisher","unstructured":"Xin J, Chen G, Hai Y (2009) A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. In: Proceeding of the second international joint conference on Computational Sciences and Optimization (CSO 2009), Sanya, Hainan, China, pp 505\u2013508, https:\/\/doi.org\/10.1109\/CSO.2009.420","DOI":"10.1109\/CSO.2009.420"},{"key":"5502_CR93","doi-asserted-by":"publisher","unstructured":"Xin J, Li S, Sheng J et al (2019) Application of improved particle swarm optimization for navigation of unmanned surface vehicles. Sensors 19(14):1\u201321. https:\/\/doi.org\/10.3390\/s19143096","DOI":"10.3390\/s19143096"},{"issue":"1","key":"5502_CR94","doi-asserted-by":"publisher","first-page":"012195","DOI":"10.1088\/1742-6596\/1754\/1\/012195","volume":"1754","author":"W Yang","year":"2021","unstructured":"Yang W, Zhou X (2021) Luo Y (2021) Simultaneously optimizing inertia weight and acceleration coefficients via introducing new functions into pso algorithm. J Phys Conf Ser 1754(1):012195. https:\/\/doi.org\/10.1088\/1742-6596\/1754\/1\/012195","journal-title":"J Phys Conf Ser"},{"key":"5502_CR95","doi-asserted-by":"publisher","unstructured":"Yasuda K, Ide A, Iwasaki N (2003) Adaptive particle swarm optimization. In: Proceeding of 2003 IEEE int. conference on SMC, pp 1554\u20131559, https:\/\/doi.org\/10.1109\/ICSMC.2003.1244633","DOI":"10.1109\/ICSMC.2003.1244633"},{"issue":"4","key":"5502_CR96","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3604812","volume":"3","author":"D Yazdani","year":"2023","unstructured":"Yazdani D, Yazdani D, Yazdani D et al (2023) A species-based particle swarm optimization with adaptive population size and deactivation of species for dynamic optimization problems. ACM Trans Evol Learn Optim 3(4):1\u201325. https:\/\/doi.org\/10.1145\/3604812","journal-title":"ACM Trans Evol Learn Optim"},{"key":"5502_CR97","doi-asserted-by":"publisher","first-page":"5585","DOI":"10.1007\/s40747-023-01012-8","volume":"9","author":"S Yin","year":"2023","unstructured":"Yin S, Jin M, Lu H et al (2023) Reinforcement-learning-based parameter adaptation methods. Complex Intell Syst 9:5585\u20135609. https:\/\/doi.org\/10.1007\/s40747-023-01012-8","journal-title":"Complex Intell Syst"},{"key":"5502_CR98","unstructured":"Yue CT, Price KV, Suganthan PN et\u00a0al (2019) Problem definitions and evaluation criteria for the cec 2020 special session and competition on single objective bound constrained numerical optimization. Tech. rep., Computational Intelligence Laboratory, Zhengzhou University, https:\/\/github.com\/P-N-Suganthan\/2020-Bound-Constrained-Opt-Benchmark"},{"key":"5502_CR99","doi-asserted-by":"publisher","first-page":"53373","DOI":"10.1109\/ACCESS.2023.3278261","volume":"11","author":"FA Zaini","year":"2023","unstructured":"Zaini FA, Sulaima MF, Razak IAWA et al (2023) A review on the applications of pso-based algorithm in demand side management: challenges and opportunities. IEEE Access 11:53373\u201353400. https:\/\/doi.org\/10.1109\/ACCESS.2023.3278261","journal-title":"IEEE Access"},{"key":"5502_CR100","doi-asserted-by":"publisher","first-page":"495","DOI":"10.3390\/e23050495","volume":"23","author":"S Zhang","year":"2021","unstructured":"Zhang S, Tong F, Li M et al (2021) Research on multi-dimensional optimal location selection of maintenance station based on big data of vehicle trajectory. Entropy 23:495. https:\/\/doi.org\/10.3390\/e23050495","journal-title":"Entropy"},{"key":"5502_CR101","unstructured":"Zhang X, Du Y, Qin G et al (2005) Adaptive particle swarm algorithm with dynamically changing inertia weight. J Xi\u2019an Jiaotong Univ 39(19):1039\u20131042. 0253-987X, 10-1039-04"},{"key":"5502_CR102","doi-asserted-by":"publisher","unstructured":"Zhang X, Ren Y, Zhen G et al (2023) A color image contrast enhancement method based on improved pso. PLoS One 18(2):e0274054. https:\/\/doi.org\/10.1371\/journal.pone.0274054","DOI":"10.1371\/journal.pone.0274054"},{"key":"5502_CR103","doi-asserted-by":"publisher","first-page":"11833","DOI":"10.1007\/s10489-022-03994-3","volume":"53","author":"S Zhao","year":"2023","unstructured":"Zhao S, Zhang T, Ma S et al (2023) Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems. Appl Intell 53:11833\u201311860. https:\/\/doi.org\/10.1007\/s10489-022-03994-3","journal-title":"Appl Intell"},{"issue":"3","key":"5502_CR104","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1093\/jcde\/qwac039","volume":"9","author":"R Zheng","year":"2022","unstructured":"Zheng R, Zhang Y, Yang K (2022) A transfer learning-based particle swarm optimization algorithm for the traveling salesman problem. J Comput Design Eng 9(3):933\u2013948. https:\/\/doi.org\/10.1093\/jcde\/qwac039","journal-title":"J Comput Design Eng"},{"key":"5502_CR105","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1007\/s40747-022-00884-6","volume":"9","author":"J Zhong","year":"2023","unstructured":"Zhong J, Feng Y, Tang S et al (2023) A collaborative neurodynamic optimization algorithm for the traveling salesman problem. Complex Intell Syst 9:1809\u20131821. https:\/\/doi.org\/10.1007\/s40747-022-00884-6","journal-title":"Complex Intell Syst"},{"key":"5502_CR106","doi-asserted-by":"publisher","unstructured":"Zhou R, Zhang L, Fu C et al (2022) Fuzzy neural network pid strategy based on pso optimization for ph control of water and fertilizer integration. Appl Sci 12(7383):1\u201319. https:\/\/doi.org\/10.3390\/app12157383","DOI":"10.3390\/app12157383"},{"key":"5502_CR107","doi-asserted-by":"publisher","unstructured":"Zhu H, Tanabe Y, Baba T (2008) A random time-varying particle swarm optimization for the real time location systems. IEEJ Trans Electr Electron Eng 128(12):1747\u20131760. https:\/\/doi.org\/10.1541\/ieejeiss.128.1747","DOI":"10.1541\/ieejeiss.128.1747"},{"issue":"3","key":"5502_CR108","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1687814019833500","volume":"11","author":"T Zhu","year":"2019","unstructured":"Zhu T, Zheng H, Ma Z (2019) A chaotic particle swarm optimization algorithm for solving optimal power system problem of electric vehicle. Adv Mech Eng 11(3):1\u20139. https:\/\/doi.org\/10.1177\/1687814019833500","journal-title":"Adv Mech Eng"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05502-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05502-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05502-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T14:06:04Z","timestamp":1719237964000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05502-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":108,"journal-issue":{"issue":"13-14","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["5502"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05502-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 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 paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}