An Improved Whale Optimization Algorithm via Angle Penalized Distance for Automatic Train Operation
Longda Wang, Yanjie Ju, Long Guo, Gang Liu, Chunlin Li, Yan Chen
TL;DR
This paper introduces an improved whale optimization algorithm to enhance automatic train operation by optimizing speed curves for energy efficiency, punctuality, and passenger comfort.
Contribution
The novel IWOA-APD algorithm with angle-penalized distance and enhanced optimization strategies improves ATO performance significantly.
Findings
The IWOA-APD algorithm reduces IGD value by 69.1% and energy consumption by 12.5%.
Punctuality improves by 64.6% and parking accuracy by 76.5% with the new algorithm.
Passenger comfort increases by 15.9% using the proposed optimization method.
Abstract
This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance…
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Taxonomy
TopicsRailway Systems and Energy Efficiency · Maritime Navigation and Safety · Maritime Transport Emissions and Efficiency