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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.

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This paper presents a method of potential landslide risk assessment from the Dorset Southern England, United Kingdom. It concentrated on potential geological hazards and vulnerability to build a quantitative landslide risk framework in Dorset. In this study, the landslides risk assessment is centered on three components: identification of risks, calculation of risks, and evaluation of risks. Therefore, different natural environmental situations were collected using a large set of optical high-resolution satellite images and remote sensing to determine the probability of landslide occurrence and provide the quantitative model by combining the remote sensing technique. The result shows Dorset area risk level is high. The possibility of landslides mainly comes from natural conditions and human actions. The calculation of the possibility of potential landslides is based on the Bayes’ theorem. Moreover, the limitation of the probability potential risk calculation is insensitivity. The challenge is related to the temporal resolution of precipitation information.