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Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

机译:混合动力电动汽车控制的进化多目标优化:从一般优化到偏好表达

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Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with atypical setting aimed at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker's preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems.
机译:许多现实世界中的优化问题都具有三个以上的目标,这引发了越来越多的研究兴趣,以开发用于解决多目标优化问题的高效进化算法。但是,大多数多目标进化算法仅在基准测试功能上进行了评估,很少应用于实际优化问题。为了向前迈进,本文提出了一个案例研究,该案例使用三种最新算法来解决多目标混合电动汽车控制器设计问题,即基于分解的进化算法(MOEA / D),一种非分解算法。支配的基于排序的遗传算法(NSGA-III)和参考矢量引导的进化算法(RVEA)。我们从非典型设置开始,旨在逼近帕累托阵线而不引入任何用户偏好。在对近似Pareto前沿进行分析的基础上,我们引入了一种偏好表达方法,并将其嵌入三种进化算法中,以识别决策者偏爱的解决方案。我们的实验结果表明,通过将用户偏好合并到多目标进化算法中,我们不仅能够深入了解目标之间的折衷关系,而且还能获得反映决策者偏好的高质量解决方案。此外,我们的实验结果表明,本文研究的三种算法中的每一种都有其独特的优势,当将这些算法应用于实际问题的优化时可以加以利用。

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