
Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02712 - e02712
Published: May 1, 2025
Language: Английский
Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02712 - e02712
Published: May 1, 2025
Language: Английский
Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121597 - 121597
Published: Sept. 16, 2023
Language: Английский
Citations
68Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)
Published: Jan. 28, 2025
Language: Английский
Citations
5Applied Soft Computing, Journal Year: 2023, Volume and Issue: 145, P. 110561 - 110561
Published: June 21, 2023
Language: Английский
Citations
38Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 26, 2025
Addressing the shortcomings of Sparrow Search Algorithm (SSA), such as low accuracy convergence and tendency falling into local optimum, a Multi-strategy Integrated (MISSA) is proposed. In this method, by improving black-winged kite algorithm applying it to producer's position update formula, an improved search strategy (ISS) firstly proposed enhance ability. Secondly, new inspired Coot algorithm, called group follow (GFS), improve ability jump out optimum. Finally, random opposition-based learning (ROBLS) applied population after each iteration its diversity. To verify MISSA's effectiveness, extensive testing conducted on 24 benchmark functions well CEC 2017 functions. The experimental results, complemented Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA other advanced optimization algorithms, exhibiting superior overall performance.
Language: Английский
Citations
2Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)
Published: March 21, 2025
Language: Английский
Citations
2Symmetry, Journal Year: 2024, Volume and Issue: 16(6), P. 661 - 661
Published: May 27, 2024
Particle swarm optimization (PSO) as a intelligence-based algorithm has been widely applied to solve various real-world problems. However, traditional PSO algorithms encounter issues such premature convergence and an imbalance between global exploration local exploitation capabilities when dealing with complex tasks. To address these shortcomings, enhanced incorporating velocity pausing adaptive strategies is proposed. By leveraging the search characteristics of terminal replacement mechanism, problem inherent in standard mitigated. The further refines controls space particle through time-varying inertia coefficients, symmetric cooperative swarms concepts, strategies, balancing exploitation. performance VASPSO was validated on 29 functions from Cec2017, comparing it against five variants seven intelligence algorithms. Experimental results demonstrate that exhibits considerable competitiveness compared 12 relevant code can be found our project homepage.
Language: Английский
Citations
7Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 73, P. 108986 - 108986
Published: Sept. 18, 2023
Language: Английский
Citations
15Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 285, P. 111351 - 111351
Published: Dec. 28, 2023
Language: Английский
Citations
14Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 12 - 42
Published: April 10, 2024
Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional engineering challenges. The primary challenge of lies in striking a balance between exploring wide search space focusing on specific regions. Meanwhile, design problems are intricate come with various constraints. This research introduces novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior hippos, designed address real-world HSO encompasses four distinct strategies based hippos different scenarios: starvation search, alpha margination, competition. To assess effectiveness HSO, we conducted experiments using CEC2017 test set, featuring highest dimensional problems, CEC2022 constrained problems. parallel, employed 14 established as control group. experimental outcomes reveal that outperforms well-known algorithms, achieving first average ranking out them CEC2022. Across classical consistently delivers best results. These results substantiate highly effective algorithm for both
Language: Английский
Citations
6Applied Soft Computing, Journal Year: 2023, Volume and Issue: 146, P. 110699 - 110699
Published: Aug. 2, 2023
Language: Английский
Citations
12