Application of the Multi-Strategy Improved Walrus Optimization Algorithm in Mobile Robot Path Planning

With the widespread application of mobile robotics technology, path planning has increasingly become a research hotspot. In complex environments, planning an efficient, stable, and safe path is an urgent problem that needs to be addressed. To this end, this paper proposes the Improved Walrus Optimization Algorithm (IWaOA) and applies it to path planning. Firstly, the Sine-Tent-Cosine chaotic mapping is used to initialize the walrus population, addressing the issue of insufficient population diversity in the later stages of the algorithm’s iteration. Next, two improvement strategies are proposed: optimal value-enhanced random walk and directional evolutionary mutation. These strategies aim to enhance the algorithm’s local search capability and precision, optimizing the issues of the original algorithm’s proneness to falling into local optima and slow convergence speed. Finally, building on the three stages of the Walrus Optimization Algorithm (WaOA), this paper introduces a fourth stage termed the “Hunting Stage” to the original algorithm with historical experience positions. It’s capable of significantly improving the overall performance of the algorithm. Evaluating the performance of the proposed algorithm, this paper conducts experiments with three distinct sets of benchmark functions and compares the outcomes against various swarm intelligence algorithms. Furthermore, the IWaOA was applied to the path planning problem for mobile robots. The experimental results confirm the efficacy and advantage of the IWaOA compared to the traditional WaOA, demonstrating a decrease in path length by 16.7%, 3.7%, and 6.2% across three different map scenarios.