Evolving Systems, Journal Year: 2024, Volume and Issue: 15(4), P. 1399 - 1426
Published: Feb. 15, 2024
Language: Английский
Evolving Systems, Journal Year: 2024, Volume and Issue: 15(4), P. 1399 - 1426
Published: Feb. 15, 2024
Language: Английский
Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 191 - 191
Published: May 4, 2023
Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic derived from the distant sense of hearing sand cats, which shows excellent performance in some large-scale problems. However, SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, tendency to be trapped topical optimum. To escape these demerits, an adaptive based on Cauchy mutation optimal neighborhood disturbance strategy (COSCSO) are provided this study. First foremost, introduction nonlinear parameter favor scaling up global search helps retrieve optimum colossal space, preventing it being caught Secondly, operator perturbs step, accelerating speed improving efficiency. Finally, diversifies population, broadens enhances exploitation. reveal COSCSO, was compared with alternative algorithms CEC2017 CEC2020 competition suites. Furthermore, COSCSO is further deployed solve six engineering The experimental results that strongly competitive capable practical
Language: Английский
Citations
23Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(15), P. 8775 - 8823
Published: March 5, 2024
Language: Английский
Citations
14Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3647 - 3697
Published: March 27, 2024
Language: Английский
Citations
14Mathematics and Computers in Simulation, Journal Year: 2024, Volume and Issue: 220, P. 65 - 88
Published: Jan. 19, 2024
Language: Английский
Citations
10Evolving Systems, Journal Year: 2024, Volume and Issue: 15(4), P. 1399 - 1426
Published: Feb. 15, 2024
Language: Английский
Citations
9