An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm

Abstract

End-to-end obstacle avoidance path planning for intelligent vehicles has been a widely studied topic. To resolve the typical issues of the solving algorithms, which are weak global optimization ability, ease in falling into local optimization and slow convergence speed, an efficient optimization method is proposed in this paper, based on the whale optimization algorithm. We present an adaptive adjustment mechanism which can dynamically modify search behavior during the iteration process of the whale optimization algorithm. Meanwhile, in order to coordinate the global optimum and local optimum of the solving algorithm, we introduce a controllable variable which can be reset according to specific routing scenarios. The evolutionary strategy of differential variation is also applied in the algorithm presented to further update the location of search individuals. In numerical experiments, we compared the proposed algorithm with the following six well-known swarm intelligence optimization algorithms: Particle Swarm Optimization (PSO), Bat Algorithm (BA), Gray Wolf Optimization Algorithm (GWO), Dragonfly Algorithm (DA), Ant Lion Algorithm (ALO), and the traditional Whale Optimization Algorithm (WOA). Our method gave rise to better results for the typical twenty-three benchmark functions. In regard to path planning problems, we observed an average improvement of 18.95% in achieving optimal solutions and 77.86% in stability. Moreover, our method exhibited faster convergence compared to some existing approaches.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

“…Noussaiba et al [6] constructed a heterogeneous algorithm called Ant Colony Optimization with Pheromone Termites (ACO-PT), which combined two state-of-the-art algorithms, namely Pheromone Termites (PT) and Ant Colony Optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. Wang et al [7] proposed an adaptive adjustment mechanism to address the typical problems of weak global optimization ability, easy falling into local optimization and slow convergence speed in the intelligent vehicle route solving algorithms, and improved the Whale optimization algorithm to enhance its operational ability. It has better convergence ability compared to other algorithms.…”

“…It is not aimed at improving a certain algorithm, but at building a new method with the goal of tourist interests and reducing travel costs. Reference [7] also optimizes and improves the algorithm to improve its convergence performance, which is different from the goal of the method we constructed. Reference [8] is mainly aimed at optimizing the transportation efficiency of the road network and improving the transportation capacity of vehicles.…”

“…The maximum value is stored at the root node, the second maximum value is stored at the leftmost child node of the lower level, and the minimum value is stored at the rightmost child node of the lower level; Subfigure (5) shows the fifth step of the algorithm: comparing the recommendation degrees of all POIs. The larger value is stored in the upper level or left child node, and the smaller value is stored in the lower level or right child node, so that the recommendation degree of any upper-level child node is greater than that of the lower level, and the recommendation degree of any left child node in any level is larger than that of the right child node; Subfigure (6) shows the sixth step of the algorithm: constructing the second decision tree; Subfigure (7) shows the seventh step of the algorithm: constructing the decision trees for all the natural attributes to form a decision forest.…”

“…Figure 3 shows a spatial decision forest f orest•G N(i) constructed by k 1 number of spatial decision trees. degree of any upper-level child node is greater than that of the lower level, and the recommendation degree of any left child node in any level is larger than that of the right child node; Subfigure (6) shows the sixth step of the algorithm: constructing the second decision tree; Subfigure (7) shows the seventh step of the algorithm: constructing the decision trees for all the natural attributes to form a decision forest. Step 2: Calculate the…”

A Wireless Channel Equalization Method Based on Hybrid Whale Optimization: For Constant Modulus Blind Equalization System

“…Remarkably, WOA also has the disadvantage of low search accuracy and suffers from entrapment in local optima [33,34]. Consequently, to improve the optimization performance of the WOA and further improve the solving efficiency, corresponding improvements of the WOA are made before the actual optimization problem.…”