Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning
In order to overcome the inherent drawbacks of the baseline Zebra Optimization Algorithm (ZOA) approach, such as its propensity for premature convergence and local optima trapping, this work creates a Multi-Strategy Enhanced Zebra Optimization Algorithm (MZOA). Three strategic changes are incorporat...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2313-7673/10/6/354 |
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| author | Zhengzong Wang Xiantao Ye Guolin Jiang Yiru Yi |
| author_facet | Zhengzong Wang Xiantao Ye Guolin Jiang Yiru Yi |
| author_sort | Zhengzong Wang |
| collection | DOAJ |
| description | In order to overcome the inherent drawbacks of the baseline Zebra Optimization Algorithm (ZOA) approach, such as its propensity for premature convergence and local optima trapping, this work creates a Multi-Strategy Enhanced Zebra Optimization Algorithm (MZOA). Three strategic changes are incorporated into the improved framework: triangular walk operators to balance localized exploitation and global exploration across optimization phases; Levy flight mechanisms to strengthen solution space traversal capabilities; and lens imaging inversion learning to improve population diversity and avoid local convergence stagnation. The enhanced solution accuracy of the MZOA over modern metaheuristics is empirically validated using the CEC2005 and CEC2017 benchmark suites. The proposed MZOA’s performance improved by 15.8% compared to the basic ZOA The algorithm’s practical effectiveness across a range of environmental difficulties is confirmed by extensive assessment in engineering optimization and robotic route planning scenarios. It routinely achieves optimal solutions in both simple and complicated setups. In robot path planning, the proposed MZOA reduces the movement path by 8.7% compared to the basic ZOA. These comprehensive evaluations establish the MZOA as a robust computational algorithm for complex optimization challenges, demonstrating enhanced convergence characteristics and operational reliability in synthetic and real-world applications. |
| format | Article |
| id | doaj-art-7a78ef1d29584cd4be4d156f8b586d7f |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-7a78ef1d29584cd4be4d156f8b586d7f2025-08-20T02:24:26ZengMDPI AGBiomimetics2313-76732025-06-0110635410.3390/biomimetics10060354Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path PlanningZhengzong Wang0Xiantao Ye1Guolin Jiang2Yiru Yi3School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, ChinaZhejiang Zhengli Enterprise Management Co., Ltd., Wenzhou 325035, ChinaZhejiang Zhengli Enterprise Management Co., Ltd., Wenzhou 325035, ChinaRuian Security Group Co., Ltd., Wenzhou 325200, ChinaIn order to overcome the inherent drawbacks of the baseline Zebra Optimization Algorithm (ZOA) approach, such as its propensity for premature convergence and local optima trapping, this work creates a Multi-Strategy Enhanced Zebra Optimization Algorithm (MZOA). Three strategic changes are incorporated into the improved framework: triangular walk operators to balance localized exploitation and global exploration across optimization phases; Levy flight mechanisms to strengthen solution space traversal capabilities; and lens imaging inversion learning to improve population diversity and avoid local convergence stagnation. The enhanced solution accuracy of the MZOA over modern metaheuristics is empirically validated using the CEC2005 and CEC2017 benchmark suites. The proposed MZOA’s performance improved by 15.8% compared to the basic ZOA The algorithm’s practical effectiveness across a range of environmental difficulties is confirmed by extensive assessment in engineering optimization and robotic route planning scenarios. It routinely achieves optimal solutions in both simple and complicated setups. In robot path planning, the proposed MZOA reduces the movement path by 8.7% compared to the basic ZOA. These comprehensive evaluations establish the MZOA as a robust computational algorithm for complex optimization challenges, demonstrating enhanced convergence characteristics and operational reliability in synthetic and real-world applications.https://www.mdpi.com/2313-7673/10/6/354zebra optimization algorithmlevy flightpath planningengineering problems |
| spellingShingle | Zhengzong Wang Xiantao Ye Guolin Jiang Yiru Yi Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning Biomimetics zebra optimization algorithm levy flight path planning engineering problems |
| title | Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning |
| title_full | Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning |
| title_fullStr | Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning |
| title_full_unstemmed | Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning |
| title_short | Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning |
| title_sort | improved zebra optimization algorithm with multi strategy fusion and its application in robot path planning |
| topic | zebra optimization algorithm levy flight path planning engineering problems |
| url | https://www.mdpi.com/2313-7673/10/6/354 |
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