Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid Car Control?

Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowled...

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Bibliographic Details
Published inApplications of Evolutionary Computation pp. 789 - 802
Main Authors Rodemann, Tobias, Nishikawa, Ken
Format Book Chapter
LanguageEnglish
Japanese
Published Cham Springer International Publishing 2016
SeriesLecture Notes in Computer Science
Subjects
Dynamic programming
Evolutionary algorithms
Hybrid cars
Online AccessGet full text
ISBN3319312030
9783319312033
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-31204-0_50

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Summary:Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowledge Evolutionary Algorithms (EAs) have so far not been used for this setting, due to the success of DP. In this work we compare DP and EA for a well-studied example and find that for the basic scenario EA is indeed clearly outperformed by DP in terms of calculation time and quality of solutions. But, we also find that when going beyond the standard scenario towards more realistic (and complex) scenarios, EAs can actually deliver a performance en par or in some cases even exceeding DP, making them useful in a number of relevant application scenarios.
ISBN:3319312030
9783319312033
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-31204-0_50