Katsa: Knn Ameliorated Tree-Seed Algorithm for Complex Optimization Problems DOI
Jianhua Jiang, Jiaqi Wu, Xianqiu Meng

et al.

Published: Jan. 1, 2023

Tree-Seed Algorithm (TSA) is an outstanding algorithm for optimization problems, but it inevitably falls into the local optimum and has a low convergence speed in solving complex problems. This paper aims to address above defects. Inspired by efficient learning from neighbors, K-Nearest Neighbor (KNN) mechanism adopted enhance tree or seed generation methods achieving balance between exploitation exploration. The proposed named KNN Ameliorated (KATSA). First, inspired mechanism, based on best tree, search space divided non-best neighbor areas. Based this division approach, strategy precise heuristic, can be accelerated. Second, migration integrate dynamic regulation which reduces possibility of falling optimum. Third, feedback effectively exploration exploitation. With enhancement KATSA converge global optima more during process. results obtained IEEE CEC 2014 benchmark functions verify excellent performance when compared with some recent variants, including STSA, EST-TSA, fb\_TSA MTSA. In addition, GWO, PSO, BOA, BA, GA, LSHADE RSA are also comparative experiments. applicability demonstrated 3 real constrained problems TSA, fb\_TSA, LSHADE, RSA, ABC PSO. experimental show that obtain stable these

Language: Английский

Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm DOI Creative Commons
Mehmet Beşkirli, Mustafa Servet Kıran

Biomimetics, Journal Year: 2023, Volume and Issue: 8(7), P. 540 - 540

Published: Nov. 11, 2023

Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals permeable in a certain frequency range. Attenuation occurs outside this There two types of filters: passive and active. Active filters consist active components, including transistors operational amplifiers, but also require power supply. contrast, only resistors capacitors. Therefore, capable generating signal gain possess the benefit high-input low-output impedance. order for to be more functional, parameters capacitors circuit must at optimum values. is discussed study. study, tree seed algorithm (TSA), plant-based optimization algorithm, used optimize with tenth-order Butterworth Bessel topology. improve performance TSA parameter optimization, opposition-based learning (OBL) added form an improved (I-TSA). The results obtained compared both basic some algorithms. experimental show I-TSA method applicable problem by performing successful prediction process.

Language: Английский

Citations

11
A diversity enhanced tree-seed algorithm based on double search with genetic and automated learning search strategies for image segmentation DOI
Xianqiu Meng, Gaochao Xu, Xu Xu

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113143 - 113143

Published: April 1, 2025

Language: Английский

Citations

0
DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies DOI Open Access
Jianhua Jiang, Jiansheng Huang, Jiaqi Wu

et al.

Symmetry, Journal Year: 2024, Volume and Issue: 16(7), P. 795 - 795

Published: June 25, 2024

The Tree-Seed Algorithm (TSA) has been effective in addressing a multitude of optimization issues. However, it faced challenges with early convergence and difficulties managing high-dimensional, intricate problems. To tackle these shortcomings, this paper introduces TSA variant (DTSA). DTSA incorporates suite methodological enhancements that significantly bolster TSA’s capabilities. It the PSO-inspired seed generation mechanism, which draws inspiration from Particle Swarm Optimization (PSO) to integrate velocity vectors, thereby enhancing algorithm’s ability explore exploit solution spaces. Moreover, DTSA’s adaptive adaptation mechanism based on count parameters employs counter dynamically adjust effectively curbing risk premature strategically reversing vectors evade local optima. also integrates trees population integrated evolutionary strategy, leverages arithmetic crossover natural selection diversity, accelerate convergence, improve accuracy. Through experimental validation IEEE CEC 2014 benchmark functions, demonstrated its enhanced performance, outperforming recent variants like STSA, EST-TSA, fb-TSA, MTSA, as well established algorithms such GWO, PSO, BOA, GA, RSA. In addition, study analyzed best value, mean, standard deviation demonstrate efficiency stability handling complex issues, robustness are proven through successful application five complex, constrained engineering scenarios, demonstrating superiority over traditional by optimizing solutions overcoming inherent limitations.

Language: Английский

Citations

3
Assessing Diversity in Global Optimization Methods DOI
Oleg Kuzenkov

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 109 - 123

Published: Jan. 1, 2025

Language: Английский

Citations

0
KATSA: KNN Ameliorated Tree Seed Algorithm for complex optimization problems DOI
Jianhua Jiang,

Jiaqi Wu,

Jinmeng Luo

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127465 - 127465

Published: April 1, 2025

Language: Английский

Citations

0
A multi-strategy boosted bald eagle search algorithm for global optimization and constrained engineering problems: case study on MLP classification problems DOI Creative Commons
Rong Zheng,

Ruikang Li,

Abdelazim G. Hussien

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 58(1)

Published: Nov. 15, 2024

The Bald Eagle Search (BES) algorithm is an innovative population-based method inspired by the intelligent hunting behavior of bald eagles. While BES shows promise, it faces challenges such as susceptibility to local optima and imbalances between exploration exploitation phases. To address these limitations, this paper introduces Multi-Strategy Boosted (MBBES) algorithm. MBBES enhances original incorporating adaptive parameter, two distinct mutation strategies, replacing swoop stage with a fall stage. We rigorously evaluate against classic improved algorithms using CEC2014 CEC2017 test sets. experimental results demonstrate that significantly improves ability escape achieves superior convergence accuracy. Moreover, ranks first according Friedman test, outperforming its counterparts in solving five practical engineering problems three MLP classification problems, underscoring effectiveness real-world optimization scenarios. These findings indicate not only surpasses but also sets new benchmark performance.

Language: Английский

Citations

1
Katsa: Knn Ameliorated Tree-Seed Algorithm for Complex Optimization Problems DOI
Jianhua Jiang, Jiaqi Wu, Xianqiu Meng

et al.

Published: Jan. 1, 2023

Tree-Seed Algorithm (TSA) is an outstanding algorithm for optimization problems, but it inevitably falls into the local optimum and has a low convergence speed in solving complex problems. This paper aims to address above defects. Inspired by efficient learning from neighbors, K-Nearest Neighbor (KNN) mechanism adopted enhance tree or seed generation methods achieving balance between exploitation exploration. The proposed named KNN Ameliorated (KATSA). First, inspired mechanism, based on best tree, search space divided non-best neighbor areas. Based this division approach, strategy precise heuristic, can be accelerated. Second, migration integrate dynamic regulation which reduces possibility of falling optimum. Third, feedback effectively exploration exploitation. With enhancement KATSA converge global optima more during process. results obtained IEEE CEC 2014 benchmark functions verify excellent performance when compared with some recent variants, including STSA, EST-TSA, fb\_TSA MTSA. In addition, GWO, PSO, BOA, BA, GA, LSHADE RSA are also comparative experiments. applicability demonstrated 3 real constrained problems TSA, fb\_TSA, LSHADE, RSA, ABC PSO. experimental show that obtain stable these

Language: Английский

Citations

1