An improved adaptive optimization approach for mobile agent-based multimodal path planning

Abstract

Path planning is a core technology in robotics, autonomous driving systems, and unmanned aerial vehicle navigation. However, in complex environments with multiple constraints, existing intelligent optimization methods are still susceptible to factors such as uneven initial distribution, insufficient environmental feedback, and premature convergence, making it difficult to stably obtain high-quality feasible paths. To address the above problems, this paper proposes an Enhanced Competition of Tribes and Cooperation of Members Method (ECTCM) based on the competition-cooperation search framework of the Competition of Tribes and Cooperation of Members Method (CTCM), aiming to improve the global exploration capability, environmental adaptability, and local exploitation capability in complex path planning tasks. Specifically, this paper designs a double-trigonometric coupled chaotic map and combines it with opposition-based learning to construct a chaotic opposition-based initialization strategy, aiming to improve the distribution uniformity of the initial population and the coverage of the search space. An environmental quality assessment mechanism is proposed to dynamically adjust the intensity of inter-tribal competition and migration according to the environmental quality of different search regions, thereby enhancing the adaptiveness of the search direction. An improved adaptive mutation strategy is introduced to adjust the perturbation intensity based on historical memory and individual ranking information, so as to alleviate population stagnation and premature convergence. Meanwhile, an elite-guided learning evolution strategy is designed to guide inferior individuals using a weighted elite center, thereby strengthening late-stage local exploitation capability. To verify the effectiveness of the proposed method, this paper compares ECTCM with 11 mainstream optimization algorithms on the CEC2020 (D = 50, 100) and CEC2022 (D = 10, 20) benchmark test functions, and further applies it to two-dimensional (2D) grid and three-dimensional (3D) UAV path planning tasks. The experimental results show that ECTCM achieves the lowest Friedman average rank in all benchmark test scenarios, and its average win rates based on the Wilcoxon rank-sum test reach 89.1%, 92.7%, 79.5%, and 83.3%, respectively. In the path planning experiments, compared with the original CTCM, ECTCM achieves comprehensive improvement rates of 6.84% and 21.36% in the 2D grid map and 3D UAV path planning scenarios, respectively. The above results indicate that, while introducing additional computational overhead, ECTCM can improve path quality, convergence accuracy, and search stability, providing a competitive optimization method for agent path planning in complex simulation environments.