A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning DOI Creative Commons

X.B. Liu,

Fufu Wang,

Yu Liu

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(2), P. 118 - 118

Published: Feb. 5, 2025

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach find a balanced solution that mitigates effects of subjective weighting, utilizing multi-objective optimization algorithm address complex planning issues involve multiple machines. Here, we introduce an advanced mathematical model cooperative path among UAVs in urban logistics scenarios, employing non-dominated sorting black-winged kite (NSBKA) this challenge. To evaluate efficacy NSBKA, was benchmarked against other algorithms using Zitzler, Deb, and Thiele (ZDT) test problems, Thiele, Laumanns, Zitzler (DTLZ) functions from conference on evolutionary computation 2009 (CEC2009) three types problems. Comparative analyses statistical results indicate proposed outperforms all 22 functions. verify capability NSBKA addressing multi-UAV problem model, applied solve problem. Simulation experiments five show can obtain reasonable collaborative set UAVs. Moreover, based generally superior terms energy saving, safety, computing efficiency during planning. This affirms effectiveness meta-heuristic dealing with objective cooperation problems further enhances robustness competitiveness NSBKA.

Language: Английский

An improved sparrow search algorithm with multi-strategy integration DOI Creative Commons
Zongyao Wang, Q. Y. Peng, Wei Rao

et al.

Scientific 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

0
Study on displacement by integrating acceleration of vibration screening excitation platform based on EBKA-TVF-EMD DOI
Chenggang Deng, Hao Dong

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e42264 - e42264

Published: Jan. 27, 2025

Language: Английский

Citations

0
A boosted African vultures optimization algorithm combined with logarithmic weight inspired novel dynamic chaotic opposite learning strategy DOI
Vanisree Chandran, Prabhujit Mohapatra

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126532 - 126532

Published: Jan. 1, 2025

Language: Английский

Citations

0
Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification DOI Creative Commons
Yu Zhu, Mingxu Zhang, Qing Huang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

Abstract The classification of chronic diseases has long been a prominent research focus in the field public health, with widespread application machine learning algorithms. Diabetes is one high prevalence worldwide and considered disease its own right. Given nature this condition, numerous researchers are striving to develop robust algorithms for accurate classification. This study introduces revolutionary approach accurately classifying diabetes, aiming provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) proposed combination Kernel Extreme Learning Machine (KELM) diabetes prediction model. First, (SBOA) enhanced by integrating particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, quantum computing-based t-distribution variations. performance QHSBOA validated using CEC2017 benchmark suite. Subsequently, used optimize kernel penalty parameter $$\:C$$ bandwidth $$\:c$$ KELM. Comparative experiments other models conducted datasets. experimental results indicate that QHSBOA-KELM model outperforms comparative four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, specificity. offers an effective method early diagnosis diabetes.

Language: Английский

Citations

0
A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning DOI Creative Commons

X.B. Liu,

Fufu Wang,

Yu Liu

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(2), P. 118 - 118

Published: Feb. 5, 2025

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach find a balanced solution that mitigates effects of subjective weighting, utilizing multi-objective optimization algorithm address complex planning issues involve multiple machines. Here, we introduce an advanced mathematical model cooperative path among UAVs in urban logistics scenarios, employing non-dominated sorting black-winged kite (NSBKA) this challenge. To evaluate efficacy NSBKA, was benchmarked against other algorithms using Zitzler, Deb, and Thiele (ZDT) test problems, Thiele, Laumanns, Zitzler (DTLZ) functions from conference on evolutionary computation 2009 (CEC2009) three types problems. Comparative analyses statistical results indicate proposed outperforms all 22 functions. verify capability NSBKA addressing multi-UAV problem model, applied solve problem. Simulation experiments five show can obtain reasonable collaborative set UAVs. Moreover, based generally superior terms energy saving, safety, computing efficiency during planning. This affirms effectiveness meta-heuristic dealing with objective cooperation problems further enhances robustness competitiveness NSBKA.

Language: Английский

Citations

0