An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design DOI Creative Commons

Shiqi Xu,

Wei Jiang, Yi Chen

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(6), P. 139 - 179

Published: Oct. 18, 2024

Abstract RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, has certain limitations. For example, it prone to falling into Local Optima, thus failing find the Global and problem of slow convergence. To solve these problems, this paper introduces an improved (PCRIME), which combines random reselection strategy Powell mechanism. The enhances population diversity helps escape while mechanism improve convergence accuracy optimal solution. verify superior performance PCRIME, we conducted series experiments at CEC 2017 2022, including qualitative analysis, ablation studies, parameter sensitivity comparison with various advanced algorithms. We used Wilcoxon signed-rank test Friedman confirm advantage PCRIME over its peers. experimental data show ability robustness. Finally, applies five real engineering problems proposes feasible solutions comprehensive index definitions for prove stability proposed algorithm. results can not only effectively practical but also excellent stability, making

Language: Английский

Enhancing slime mould algorithm for engineering optimization: leveraging covariance matrix adaptation and best position management DOI Creative Commons

Jinpeng Huang,

Yi Chen, Ali Asghar Heidari

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(4), P. 151 - 183

Published: June 12, 2024

Abstract The slime mould algorithm (SMA), as an emerging and promising swarm intelligence algorithm, has been studied in various fields. However, SMA suffers from issues such easily getting trapped local optima slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes improved SMA, named HESMA, by incorporating the covariance matrix adaptation evolution strategy (CMA-ES) storing best position of each individual (SBP). On one hand, CMA-ES enhances algorithm’s exploration capability, addressing issue being unable explore vicinity optimal solution. other SBP convergence speed prevents it diverging inferior solutions. Finally, validate effectiveness our proposed conducted experiments on 30 IEEE CEC 2017 benchmark functions compared HESMA with 12 conventional metaheuristic algorithms. results demonstrated that indeed achieved improvements over SMA. Furthermore, highlight performance further, 13 advanced algorithms, showed outperformed these algorithms significantly. Next, five engineering optimization problems, experimental revealed exhibited significant advantages solving real-world These findings further support practicality complex design challenges.

Language: Английский

Citations

6
IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection DOI Creative Commons

Jinpeng Huang,

Yi Chen, Ali Asghar Heidari

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(8), P. 110561 - 110561

Published: July 22, 2024

Rime optimization algorithm (RIME) encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, low convergence accuracy when handling problems. This paper introduces a variant of RIME called IRIME address these drawbacks. integrates the soft besiege (SB) composite mutation strategy (CMS) restart (RS). To comprehensively validate IRIME's performance, IEEE CEC 2017 benchmark tests were conducted, comparing it against many advanced algorithms. The results indicate that performance is best. In addition, applying in four engineering problems reflects solving practical Finally, proposes binary version, bIRIME, can be applied feature selection bIRIMR performs well on 12 low-dimensional datasets 24 high-dimensional datasets. It outperforms other algorithms terms number subsets classification accuracy. conclusion, bIRIME has great potential selection.

Language: Английский

Citations

4
Application of Lévy and sine cosine algorithm hunger game search in machine learning model parameter optimization and acute appendicitis prediction DOI Creative Commons
Shizheng Qu, Huan Liu, Hanwen Zhang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126413 - 126413

Published: Jan. 1, 2025

Language: Английский

Citations

0
Game-Theory-Based Multi-Objective Optimization for Enhancing Environmental and Social Life Cycle Assessment in Steel–Concrete Composite Bridges DOI Creative Commons
David Martínez-Muñoz, José V. Martí, Víctor Yepes

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(2), P. 273 - 273

Published: Jan. 16, 2025

The design of bridges must balance sustainability and construction simplicity. A game-theory-based optimization method was applied in this research to find a sustainable steel–concrete composite bridge design. evaluated through cost environmental social impact using the Life Cycle Assessment method. process considered four criteria simultaneously, discrete version SCA algorithm transfer function for discretization. preferred solutions were selected Minkowski distances approach. Results showed decrease slab reinforcement an increase amount steel cross-section, leading only 8.2‰ compared similar studies. Regarding geometry obtained considers cells upper lower parts webs improve bending resistance. proposed allows simultaneous multiple provides yet simple solution.

Language: Английский

Citations

0
Quadruple Strategy-Driven Hiking Optimization Algorithm for Low and High-Dimensional Feature Selection and Real-World Skin Cancer Classification DOI
Mahmoud Abdel-Salam, Saleh Ali Alomari,

Mohammad H. Almomani

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113286 - 113286

Published: March 1, 2025

Language: Английский

Citations

0
An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design DOI Creative Commons

Shiqi Xu,

Wei Jiang, Yi Chen

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(6), P. 139 - 179

Published: Oct. 18, 2024

Abstract RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, has certain limitations. For example, it prone to falling into Local Optima, thus failing find the Global and problem of slow convergence. To solve these problems, this paper introduces an improved (PCRIME), which combines random reselection strategy Powell mechanism. The enhances population diversity helps escape while mechanism improve convergence accuracy optimal solution. verify superior performance PCRIME, we conducted series experiments at CEC 2017 2022, including qualitative analysis, ablation studies, parameter sensitivity comparison with various advanced algorithms. We used Wilcoxon signed-rank test Friedman confirm advantage PCRIME over its peers. experimental data show ability robustness. Finally, applies five real engineering problems proposes feasible solutions comprehensive index definitions for prove stability proposed algorithm. results can not only effectively practical but also excellent stability, making

Language: Английский

Citations

1