Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

Abstract

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm’s solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications.

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

“…The mathematical model of this problem is expressed as follows. Min f (x) = 0.6224x 1 x 3 x 4 + 1.7781x 2 x 2 3 + 3.1661x 2 1 x 4 + 19.81x 2 1 x 3 (16) x = (x 1 , x 2 , x 3 , x 4 ) = (T s , T h , R, L) Equation ( 16) constitutes the objective function of the classical pressure vessel design problem, delineating the minimization objective. This objective pertains to the discovery of an optimal configuration for the four design variables, namely, shell thickness Ts, head thickness Th, inner radius R, and cylinder length L. In the literature, this problem is solved by using the mathematical methods of an augmented Lagrangian multiplier [55] and branch-and-bound [56] classical techniques.…”

“…Furthermore, Hu et al [14] and Boursianis et al [15] both demonstrated GWO's utility in the prediction of wind speed and the optimization of antenna design and synthesis, respectively. Liu et al [16] incorporated the Gray Wolf Optimizer (GWO) algorithm into the domain of robotic path planning. Through the integration of a suite of enhancement techniques, this approach facilitated notable advancements in the practical application of path planning, thereby offering a more efficacious optimization strategy that contributes significantly to the progression of research in robotic path planning.…”