This paper aims to contribute primarily in two areas: using multiple new strategies to devise an improved sand cat swarm optimization (ISCSO) algorithm with superior performance and exploring its applicability to the path planning issue that requires finding a safe route with the shortest length for an agricultural robot.
This paper designs and introduces multiple new strategies to modify the sand cat swarm optimization (SCSO) algorithm from different perspectives. Subsequently, 23 well-known standard benchmark function experiments and CEC2021 function experiments are performed using the ISCSO algorithm and another five approaches, encompassing the SCSO algorithm, the Harris Hawks optimization (HHO) algorithm, the GWO, the Snake Optimizer (SO) and the Zebra Optimization Algorithm (ZOA). Then, the results are analyzed to showcase the efficacy and superiority of the ISCSO algorithm. On this basis, we also explore the effect of applying the ISCSO algorithm to puzzle out the agricultural robot path planning issue.
All experimental results manifest that, except for a few functions among the 23 standard benchmark function experiments and CEC2021 function experiments, the ISCSO algorithm performs better overall than the other five algorithms with regard to optimization ability, convergence rate and stability. Moreover, the ISCSO algorithm is better suited for addressing the path planning issue encountered by the agricultural robot and exhibits stronger optimization ability in comparison to the SCSO algorithm.
This paper devised a novel improved SCSO algorithm with better performance and explored its applicability to the path planning issue that requires finding a safe route with the shortest length for an agricultural robot.
