{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T07:44:25Z","timestamp":1768635865753,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T00:00:00Z","timestamp":1719273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation of the Jilin Provincial Department of Science and Technology","award":["YDZJ202201ZYTS565"],"award-info":[{"award-number":["YDZJ202201ZYTS565"]}]},{"name":"Foundation of the Jilin Provincial Department of Science and Technology","award":["2022B84"],"award-info":[{"award-number":["2022B84"]}]},{"name":"Foundation of the Jilin Provincial Department of Science and Technology","award":["JJKH20240198KJ"],"award-info":[{"award-number":["JJKH20240198KJ"]}]},{"name":"Foundation of Social Science of Jilin Province","award":["YDZJ202201ZYTS565"],"award-info":[{"award-number":["YDZJ202201ZYTS565"]}]},{"name":"Foundation of Social Science of Jilin Province","award":["2022B84"],"award-info":[{"award-number":["2022B84"]}]},{"name":"Foundation of Social Science of Jilin Province","award":["JJKH20240198KJ"],"award-info":[{"award-number":["JJKH20240198KJ"]}]},{"name":"Jilin Provincial Department of Education Science and Technology","award":["YDZJ202201ZYTS565"],"award-info":[{"award-number":["YDZJ202201ZYTS565"]}]},{"name":"Jilin Provincial Department of Education Science and Technology","award":["2022B84"],"award-info":[{"award-number":["2022B84"]}]},{"name":"Jilin Provincial Department of Education Science and Technology","award":["JJKH20240198KJ"],"award-info":[{"award-number":["JJKH20240198KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The Tree-Seed Algorithm (TSA) has been effective in addressing a multitude of optimization issues. However, it has faced challenges with early convergence and difficulties in managing high-dimensional, intricate optimization problems. To tackle these shortcomings, this paper introduces a TSA variant (DTSA). DTSA incorporates a suite of methodological enhancements that significantly bolster TSA\u2019s capabilities. It introduces the PSO-inspired seed generation mechanism, which draws inspiration from Particle Swarm Optimization (PSO) to integrate velocity vectors, thereby enhancing the algorithm\u2019s ability to explore and exploit solution spaces. Moreover, DTSA\u2019s adaptive velocity adaptation mechanism based on count parameters employs a counter to dynamically adjust these velocity vectors, effectively curbing the risk of premature convergence and strategically reversing vectors to evade local optima. DTSA also integrates the trees population integrated evolutionary strategy, which leverages arithmetic crossover and natural selection to bolster population diversity, accelerate convergence, and improve solution accuracy. Through experimental validation on the IEEE CEC 2014 benchmark functions, DTSA has demonstrated its enhanced performance, outperforming recent TSA variants like STSA, EST-TSA, fb-TSA, and MTSA, as well as established benchmark algorithms such as GWO, PSO, BOA, GA, and RSA. In addition, the study analyzed the best value, mean, and standard deviation to demonstrate the algorithm\u2019s efficiency and stability in handling complex optimization issues, and DTSA\u2019s robustness and efficiency are proven through its successful application in five complex, constrained engineering scenarios, demonstrating its superiority over the traditional TSA by dynamically optimizing solutions and overcoming inherent limitations.<\/jats:p>","DOI":"10.3390\/sym16070795","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T13:26:43Z","timestamp":1719494803000},"page":"795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-2922","authenticated-orcid":false,"given":"Jianhua","family":"Jiang","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China"},{"name":"Jilin Province Key Laboratory of Fintech, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2473-5009","authenticated-orcid":false,"given":"Jiansheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China"},{"name":"Jilin Province Key Laboratory of Fintech, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9233-9851","authenticated-orcid":false,"given":"Jiaqi","family":"Wu","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China"},{"name":"Jilin Province Key Laboratory of Fintech, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinmeng","family":"Luo","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China"},{"name":"Jilin Province Key Laboratory of Fintech, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8001-8092","authenticated-orcid":false,"given":"Xi","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China"},{"name":"Jilin Province Key Laboratory of Fintech, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9215-4979","authenticated-orcid":false,"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by Simulated Annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1177\/003754979406200405","article-title":"Constrained optimization via genetic algorithms","volume":"62","author":"Homaifar","year":"1994","journal-title":"Simulation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0045-7825(99)00389-8","article-title":"An efficient constraint handling method for genetic algorithms","volume":"186","author":"Deb","year":"2000","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1016\/j.asoc.2010.05.007","article-title":"Two hybrid differential evolution algorithms for engineering design optimization","volume":"10","author":"Liao","year":"2010","journal-title":"Appl. Soft Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s00366-020-01111-x","article-title":"Sizing and prestress optimization of Class-2 tensegrity structures for space boom applications","volume":"38","author":"Yildiz","year":"2022","journal-title":"Eng. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100888","DOI":"10.1016\/j.swevo.2021.100888","article-title":"A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems","volume":"64","author":"Osaba","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/JAS.2021.1004129","article-title":"A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends","volume":"8","author":"Tang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_8","first-page":"1","article-title":"Evolutionary large-scale multi-objective optimization: A survey","volume":"54","author":"Tian","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1287\/ijoc.11.4.345","article-title":"Heuristic and metaheuristic approaches for a class of two-dimensional bin packing problems","volume":"11","author":"Lodi","year":"1999","journal-title":"INFORMS J. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","article-title":"Slime mould algorithm: A new method for stochastic optimization","volume":"111","author":"Li","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s10462-017-9605-z","article-title":"Metaheuristic research: A comprehensive survey","volume":"52","author":"Hussain","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5479","DOI":"10.1007\/s10462-022-10280-8","article-title":"Quantum-inspired metaheuristic algorithms: Comprehensive survey and classification","volume":"56","author":"Gharehchopogh","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1007\/s00521-020-05145-6","article-title":"A novel meta-heuristic search algorithm for solving optimization problems: Capuchin search algorithm","volume":"33","author":"Braik","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wu, J., Luo, J., Yang, X., and Huang, Z. (2024). MOBCA: Multi-Objective Besiege and Conquer Algorithm. Biomimetics, 9.","DOI":"10.3390\/biomimetics9060316"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s00500-011-0754-8","article-title":"Metaheuristic optimization frameworks: A survey and benchmarking","volume":"16","author":"Parejo","year":"2012","journal-title":"Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2949","DOI":"10.1007\/s00521-020-05107-y","article-title":"Group search optimizer: A nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications","volume":"33","author":"Abualigah","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10915-024-02518-0","article-title":"A new insight on augmented Lagrangian method with applications in machine learning","volume":"99","author":"Bai","year":"2024","journal-title":"J. Sci. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.asoc.2016.08.036","article-title":"Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing\u2013tabu search algorithm to solve the symmetrical traveling salesman problem","volume":"49","author":"Lin","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.swevo.2019.03.007","article-title":"Using compact evolutionary tabu search algorithm for matching sensor ontologies","volume":"48","author":"Xue","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1016\/j.eswa.2014.12.004","article-title":"Iterated local search embedded adaptive neighborhood selection approach for the multi-depot vehicle routing problem with simultaneous deliveries and pickups","volume":"42","author":"Li","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1016\/j.eswa.2011.08.146","article-title":"Genetic algorithm with iterated local search for solving a location-routing problem","volume":"39","author":"Derbel","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/TEVC.2008.924428","article-title":"Self-adaptive multimethod search for global optimization in real-parameter spaces","volume":"13","author":"Vrugt","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106724","DOI":"10.1016\/j.asoc.2020.106724","article-title":"Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem","volume":"100","author":"Deng","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/TEVC.2008.2009457","article-title":"Differential evolution using a neighborhood-based mutation operator","volume":"13","author":"Das","year":"2009","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","article-title":"Particle swarm optimization algorithm: An overview","volume":"22","author":"Wang","year":"2018","journal-title":"Soft Comput."},{"key":"ref_26","first-page":"931256","article-title":"A comprehensive survey on particle swarm optimization algorithm and its applications","volume":"2015","author":"Zhang","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.asoc.2014.10.020","article-title":"A directed artificial bee colony algorithm","volume":"26","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1007\/s00500-017-2547-1","article-title":"A self-adaptive artificial bee colony algorithm based on global best for global optimization","volume":"22","author":"Xue","year":"2018","journal-title":"Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4831","DOI":"10.1016\/j.cnsns.2012.05.010","article-title":"Krill herd: A new bio-inspired optimization algorithm","volume":"17","author":"Gandomi","year":"2012","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.asoc.2016.08.041","article-title":"A comprehensive review: Krill Herd algorithm (KH) and its applications","volume":"49","author":"Bolaji","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.swevo.2018.02.018","article-title":"A comprehensive survey on gravitational search algorithm","volume":"41","author":"Rashedi","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ins.2019.05.038","article-title":"An evolutionary gravitational search-based feature selection","volume":"497","author":"Taradeh","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106711","DOI":"10.1016\/j.knosys.2020.106711","article-title":"Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems","volume":"213","author":"MiarNaeimi","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6686","DOI":"10.1016\/j.eswa.2015.04.055","article-title":"TSA: Tree-seed algorithm for continuous optimization","volume":"42","author":"Kiran","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.asoc.2017.12.026","article-title":"Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons","volume":"64","author":"Hasanien","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110940","DOI":"10.1016\/j.knosys.2023.110940","article-title":"ATSA: An Adaptive Tree Seed Algorithm based on double-layer framework with tree migration and seed intelligent generation","volume":"279","author":"Jiang","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wu, J., Meng, X., Qian, L., Luo, J., and Li, K. (2024, June 01). Katsa: Knn Ameliorated Tree-Seed Algorithm for Complex Optimization Problems. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4636664.","DOI":"10.2139\/ssrn.4636664"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"118311","DOI":"10.1016\/j.eswa.2022.118311","article-title":"Enhance tree-seed algorithm using hierarchy mechanism for constrained optimization problems","volume":"209","author":"Jiang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"114579","DOI":"10.1016\/j.eswa.2021.114579","article-title":"Solving continuous optimization problems using the tree seed algorithm developed with the roulette wheel strategy","volume":"170","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6877","DOI":"10.1007\/s00521-019-04155-3","article-title":"A comparison of modified tree\u2013seed algorithm for high-dimensional numerical functions","volume":"32","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TEVC.2003.810069","article-title":"Chaotic sequences to improve the performance of evolutionary algorithms","volume":"7","author":"Caponetto","year":"2003","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"122802","DOI":"10.1016\/j.physa.2019.122802","article-title":"STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems","volume":"537","author":"Jiang","year":"2020","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.eswa.2016.05.009","article-title":"A population initialization method for evolutionary algorithms based on clustering and Cauchy deviates","volume":"60","author":"Bajer","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, L., Qian, L., Cheng, B., Hou, W., and Zhuang, Y. (2023). Sine-SSA-BP ship trajectory prediction based on chaotic mapping improved sparrow search algorithm. Sensors, 23.","DOI":"10.3390\/s23020704"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Be\u015fkirli, M., and Kiran, M.S. (2023). Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm. Biomimetics, 8.","DOI":"10.3390\/biomimetics8070540"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104303","DOI":"10.1016\/j.engappai.2021.104303","article-title":"TriTSA: Triple Tree-Seed Algorithm for dimensional continuous optimization and constrained engineering problems","volume":"104","author":"Jiang","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"18627","DOI":"10.1007\/s00500-020-05099-w","article-title":"TSASC: Tree\u2013seed algorithm with sine\u2013cosine enhancement for continuous optimization problems","volume":"24","author":"Jiang","year":"2020","journal-title":"Soft Comput."},{"key":"ref_49","unstructured":"Linden, A. (2024, June 01). ITSA: Stata Module to Perform Interrupted Time Series Analysis for Single and Multiple Groups. Available online: https:\/\/ideas.repec.org\/c\/boc\/bocode\/s457793.html."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"122323","DOI":"10.1016\/j.physa.2019.122323","article-title":"EST-TSA: An effective search tendency based to tree seed algorithm","volume":"534","author":"Jiang","year":"2019","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106314","DOI":"10.1016\/j.asoc.2020.106314","article-title":"Enhancing tree-seed algorithm via feed-back mechanism for optimizing continuous problems","volume":"92","author":"Jiang","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"153","DOI":"10.17531\/ein.2022.1.17","article-title":"Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Levy flight","volume":"24","author":"Chen","year":"2022","journal-title":"Eksploat. I Niezawodn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.asoc.2017.10.013","article-title":"A modification of tree-seed algorithm using Deb\u2019s rules for constrained optimization","volume":"63","author":"Babalik","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"107199","DOI":"10.1016\/j.knosys.2021.107199","article-title":"Development of deer hunting linked earthworm optimization algorithm for solving large scale traveling salesman problem","volume":"227","author":"Kanna","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"21582440221094586","DOI":"10.1177\/21582440221094586","article-title":"An enhanced TSA-MLP model for identifying credit default problems","volume":"12","author":"Jiang","year":"2022","journal-title":"SAGE Open"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Aslan, M.F., Sabanci, K., and Ropelewska, E. (2022). A new approach to COVID-19 detection: An ANN proposal optimized through tree-seed algorithm. Symmetry, 14.","DOI":"10.3390\/sym14071310"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1109\/TSMC.2023.3340919","article-title":"Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis","volume":"54","author":"Luo","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"129077","DOI":"10.1016\/j.energy.2023.129077","article-title":"A hybrid RSM-GA-PSO approach on optimization of process intensification of linseed biodiesel synthesis using an ultrasonic reactor: Enhancing biodiesel properties and engine characteristics with ternary fuel blends","volume":"288","author":"Ahmad","year":"2024","journal-title":"Energy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"101135","DOI":"10.1016\/j.iot.2024.101135","article-title":"A Systematic Review of Applying Grey Wolf Optimizer, its Variants, and its Developments in Different Internet of Things Applications","volume":"26","author":"Zamani","year":"2024","journal-title":"Internet Things"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.asoc.2014.11.029","article-title":"A new modification approach on bat algorithm for solving optimization problems","volume":"28","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"20263","DOI":"10.1007\/s00521-022-07575-w","article-title":"Development of L\u00e9vy flight-based reptile search algorithm with local search ability for power systems engineering design problems","volume":"34","author":"Ekinci","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"123289","DOI":"10.1016\/j.eswa.2024.123289","article-title":"Covariance matrix adaptation evolution strategy based on correlated evolution paths with application to reinforcement learning","volume":"246","author":"Ajani","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"121452","DOI":"10.1016\/j.eswa.2023.121452","article-title":"PSO-based image encryption scheme using modular integrated logistic exponential map","volume":"237","author":"Kocak","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"130771","DOI":"10.1016\/j.energy.2024.130771","article-title":"MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem","volume":"293","author":"Zheng","year":"2024","journal-title":"Energy"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.aej.2023.12.050","article-title":"EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems","volume":"87","author":"He","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.ins.2014.08.053","article-title":"A new metaheuristic for numerical function optimization: Vortex Search algorithm","volume":"293","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","article-title":"A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm","volume":"169","author":"Askarzadeh","year":"2016","journal-title":"Comput. Struct."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"106933","DOI":"10.1016\/j.asoc.2020.106933","article-title":"Dimension by dimension dynamic sine cosine algorithm for global optimization problems","volume":"98","author":"Li","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2017.12.008","article-title":"Knowledge discovery in multiobjective optimization problems in engineering via Genetic Programming","volume":"99","author":"Russo","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1016\/j.asoc.2012.03.030","article-title":"A hybrid particle swarm\u2013Nelder\u2013Mead optimization method for crack detection in cantilever beams","volume":"12","author":"Baghmisheh","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"115351","DOI":"10.1016\/j.eswa.2021.115351","article-title":"Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems","volume":"183","author":"Gupta","year":"2021","journal-title":"Expert Syst. Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/7\/795\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:03:53Z","timestamp":1760108633000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/7\/795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,25]]},"references-count":71,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["sym16070795"],"URL":"https:\/\/doi.org\/10.3390\/sym16070795","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,25]]}}}