Performance Evaluation of Three Intelligent Optimization Algorithms for Obstacle Avoidance Path Planning

Chen, Shumeng; Wang, Chia‐Hung; Dong, Zhenyu; Zhao, Qigen; Yang, Qipeng; Wei, Yi; Huang, Guoshu

doi:10.1007/978-981-16-8430-2_6

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…In future work, we anticipate that the improved k-order Markov-based real-time algorithm may be applied to other fields, such as combining it with Monte Carlo simulations of wind power output. The weight optimization of the studied PSO-BP neural network could be further improved via other intelligent optimization algorithms, such as the Whale Optimization Algorithm [36], Wolf Pack Algorithm [37], Dragonfly Algorithm, Ant Lion Algorithm, and so on. Besides, in this paper, only wind power is considered as an input variable.…”

Section: Discussionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

2023

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Wind power prediction is an important research topic in the wind power industry and many prediction algorithms have recently been studied for the sake of achieving the goal of improving the accuracy of short-term forecasting in an effective way. To tackle the issue of generating a huge transition matrix in the traditional Markov model, this paper introduces a real-time forecasting method that reduces the required calculation time and memory space without compromising the prediction accuracy of the original model. This method is capable of obtaining the state probability interval distribution for the next moment through real-time calculation while preserving the accuracy of the original model. Furthermore, the proposed Markov-based Back Propagation (BP) neural network was optimized using the Particle Swarm Optimization (PSO) algorithm in order to effectively improve the prediction approach with an improved PSO-BP neural network. Compared with traditional methods, the computing time of our improved algorithm increases linearly, instead of growing exponentially. Additionally, the optimized Markov-based PSO-BP neural network produced a better predictive effect. We observed that the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) of the prediction model were 12.7% and 179.26, respectively; compared with the existing methods, this model generates more accurate prediction results.

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Section: Discussionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

2023

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

et al. 2023

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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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Performance Evaluation of Three Intelligent Optimization Algorithms for Obstacle Avoidance Path Planning

Cited by 2 publications

References 10 publications

Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

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

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