A Multi-Strategy Cooperative Red-Billed Blue Magpie Optimizer for Robot Path Planning

Mobile robot path planning in complex environments remains challenging due to obstacle constraints, high-dimensional search space, and the need to balance path optimality and safety. To address these challenges, this paper proposes an improved Red-Billed Blue Magpie Optimizer (IRBMO) with multi-strategy cooperation. Specifically, a territorial awareness mechanism enhances global exploration to avoid premature path convergence, a representative individual learning strategy improves exploitation to refine path quality, and a random subpopulation diffusion strategy helps escape local optima in complex obstacle environments. The proposed method is applied to grid-based path planning problems with different map sizes and obstacle densities. Experimental results show that IRBMO significantly reduces path length compared with other algorithms, while achieving faster convergence and better stability. Parameter sensitivity analysis, ablation study, and convergence analysis further verify the effectiveness of the proposed strategies. In addition, benchmark tests on CEC2017 and CEC2022 functions against 19 competitors further confirm its optimization capability. Overall, IRBMO provides an effective and robust solution for robot path planning problems.