.png){kind=icon}
.png){kind=icon}
Return
Gekko Japonicus Algorithm: A Novel Nature-inspired Algorithm for Engineering Problems and Path Planning
.png){kind=icon}
PRE
AI
DOI：10.1007/s42235-025-00805-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm. The algorithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus. The mathematical model is developed by simulating various biological behaviors of the Gekko japonicus, such as hybrid locomotion patterns, directional olfactory guidance, implicit group advantage tendencies, and the tail autotomy mechanism. By integrating multi-stage mutual constraints and dynamically adjusting parameters, GJA maintains an optimal balance between global exploration and local exploitation, thereby effectively solving complex optimization problems. To assess the performance of GJA, comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets. Additionally, a Friedman test was performed on the experimental results to assess the statistical significance of differences between various algorithms. And GJA was evaluated using multiple qualitative indicators, further confirming its superiority in exploration and exploitation. Finally, GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability. Experimental results indicate that, compared to other high-performance algorithms, GJA demonstrates exceptional performance as a powerful optimization algorithm in complex optimization problems. We make the code publicly available at: https://github.com/zhy1109/Gekko-japonicusalgorithm
Keywords:
Gekko japonicus algorithm
Metaheuristic algorithm
Exploration and exploitation
Engineering optimization
Path planning
Journal
IF:
5.8
Papers:
1.9K
Citations:
4.8K