Chaos crossover quantum attraction-repulsion optimization algorithm DOI
Mingwei Li, Xiang‐Yang Li, Yutian Wang

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 92, P. 101811 - 101811

Published: Dec. 9, 2024

Language: Английский

Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method DOI
Yuchao Wang,

Kelin Tong,

Chunhai Fu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127808 - 127808

Published: April 1, 2025

Language: Английский

Citations

0
A novel reward-based golden jackal optimization algorithm uses mix-weighted dynamic and random opposition learning to solve optimization problems DOI

Sarada Mohapatra,

Priteesha Sarangi,

Prabhujit Mohapatra

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(5)

Published: April 28, 2025

Language: Английский

Citations

0
Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic system DOI Creative Commons

Dongning Chen,

Haowen Wang,

Dongbo Hu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 19, 2024

Language: Английский

Citations

2
A green hydrogen production model from solar powered water electrolyze based on deep chaotic Lévy gazelle optimization DOI Creative Commons
Heba Askr, Mahmoud Abdel-Salam, Václav Snåšel

et al.

Engineering Science and Technology an International Journal, Journal Year: 2024, Volume and Issue: 60, P. 101874 - 101874

Published: Nov. 11, 2024

Language: Английский

Citations

2
CGJO: a novel complex-valued encoding golden jackal optimization DOI Creative Commons
Jinzhong Zhang, Gang Zhang,

Min Kong

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 23, 2024

Golden jackal optimization (GJO) is inspired by mundane characteristics and collaborative hunting behaviour, which mimics foraging, trespassing encompassing, capturing prey to refresh a jackal's position. However, the GJO has several limitations, such as slow convergence rate, low computational accuracy, premature convergence, poor solution efficiency, weak exploration exploitation. To enhance global detection ability this paper proposes novel complex-valued encoding golden (CGJO) achieve function engineering design. The strategy deploys dual-diploid organization encode real imaginary portions of converts dual-dimensional region single-dimensional manifestation region, increases population diversity, restricts search stagnation, expands area, promotes information exchange, fosters collaboration efficiency improves accuracy. CGJO not only exhibits strong adaptability robustness supplementary advantages but also balances local exploitation promote precision determine best solution. CEC 2022 test suite six real-world designs are utilized evaluate effectiveness feasibility CGJO. compared with three categories existing algorithms: (1) WO, HO, NRBO BKA recently published algorithms; (2) SCSO, GJO, RGJO SGJO highly cited (3) L-SHADE, LSHADE-EpsSin CMA-ES performing algorithms. experimental results reveal that superior those other superiority reliability quicker greater computation precision, stability robustness.

Language: Английский

Citations

1
Chaos crossover quantum attraction-repulsion optimization algorithm DOI
Mingwei Li, Xiang‐Yang Li, Yutian Wang

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 92, P. 101811 - 101811

Published: Dec. 9, 2024

Language: Английский

Citations

0