Jin Zhu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jin Zhu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bojun Liu
Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
1 shared publication - Jun Zheng
Taizhou Institute of Zhejiang University, Taizhou 318000, China
1 shared publication - Meng Wang
Taizhou Institute of Zhejiang University, Taizhou 318000, China
1 shared publication - Shaojie Yin
Taizhou Institute, Zhejiang University, Taizhou 318000, China
1 shared publication
Latest Publications
- Open Access
- 12 November 2025
- Published by MDPI AG in Biomimetics
- Vol. 10 (11) , 761
An Enhanced Secretary Bird Optimization Algorithm Based on Multi Population Management for Numerical Optimization Problems
The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500 (CEC2022 10-D), 2.333 (CEC2022 20-D), 1.828 (CEC2017 50-D) and 1.931 (CEC2017 100-D). Moreover, engineering constrained optimization problems further verify its applicability to real-world optimization tasks.
- 12 November 2025
- Published by MDPI AG in Biomimetics
- Vol. 10 (11) , 761
Open AccessAn Enhanced Secretary Bird Optimization Algorithm Based on Multi Population Management for Numerical Optimization Problems
The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500 (CEC2022 10-D), 2.333 (CEC2022 20-D), 1.828 (CEC2017 50-D) and 1.931 (CEC2017 100-D). Moreover, engineering constrained optimization problems further verify its applicability to real-world optimization tasks.