An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning

Abstract

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.