Abstract: 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 balancebetween global exploration and local exploitation, thereby effectively solving complex optimization problems. To assessthe performance of GJA, comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithmsusing 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 usingmultiple qualitative indicators, further confirming its superiority in exploration and exploitation. Finally, GJA was utilizedto solve four engineering optimization problems and further implemented in robotic path planning to verify its practicalapplicability. 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 publiclyavailable at: https://github.com/zhy1109/Gekko-japonicusalgorithm

Key words: Gekko japonicus algorithm, Metaheuristic algorithm, Exploration and exploitation, Engineeringoptimization, Path planning')">Gekko japonicus algorithm, Metaheuristic algorithm, Exploration and exploitation, Engineeringoptimization, Path planning