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Article

DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies

1
Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China
2
Jilin Province Key Laboratory of Fintech, Jilin University of Finance and Economics, Changchun 130117, China
3
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
*
Author to whom correspondence should be addressed.
Symmetry 2024, 16(7), 795; https://doi.org/10.3390/sym16070795
Submission received: 26 May 2024 / Revised: 18 June 2024 / Accepted: 19 June 2024 / Published: 25 June 2024
(This article belongs to the Special Issue Advanced Optimization Methods and Their Applications)

Abstract

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’s 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’s ability to explore and exploit solution spaces. Moreover, DTSA’s 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’s efficiency and stability in handling complex optimization issues, and DTSA’s 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.
Keywords: swarm intelligence; Tree-Seed Algorithm; PSO-inspired seed generation mechanism; engineering optimization problems swarm intelligence; Tree-Seed Algorithm; PSO-inspired seed generation mechanism; engineering optimization problems

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MDPI and ACS Style

Jiang, J.; Huang, J.; Wu, J.; Luo, J.; Yang, X.; Li, W. DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies. Symmetry 2024, 16, 795. https://doi.org/10.3390/sym16070795

AMA Style

Jiang J, Huang J, Wu J, Luo J, Yang X, Li W. DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies. Symmetry. 2024; 16(7):795. https://doi.org/10.3390/sym16070795

Chicago/Turabian Style

Jiang, Jianhua, Jiansheng Huang, Jiaqi Wu, Jinmeng Luo, Xi Yang, and Weihua Li. 2024. "DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies" Symmetry 16, no. 7: 795. https://doi.org/10.3390/sym16070795

APA Style

Jiang, J., Huang, J., Wu, J., Luo, J., Yang, X., & Li, W. (2024). DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies. Symmetry, 16(7), 795. https://doi.org/10.3390/sym16070795

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