Effective multi-objective discrete optimization of Truss-Z layouts using a GPU
•A novel framework for Truss-Z layout optimization is presented.•It is based on image processing, evolutionary algorithm & massive parallelization.•GPU is used for efficient objective function evaluation in the EA.•3 case-studies are studied: Modular path, Mountain pier & Train station retro...
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| Published in: | Applied soft computing Vol. 70; pp. 501 - 512 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.09.2018
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | •A novel framework for Truss-Z layout optimization is presented.•It is based on image processing, evolutionary algorithm & massive parallelization.•GPU is used for efficient objective function evaluation in the EA.•3 case-studies are studied: Modular path, Mountain pier & Train station retrofitting
Truss-Z (TZ) is an Extremely Modular System for creating skeletal free-form ramps and ramp networks. The TZ structures are comprised of four variations of two types of basic unit subjected to rotation. The two types of units are: R and L being a mirror reflection of each other. This paper presents a novel method based on image processing, evolutionary algorithm and intensive parallelization of multi-objective optimization of TZ layouts.
The algorithm returns a sequence of modules. The result guarantees a TZ connection between two given points (regions) and minimizes the fitness function representing certain costs associated with setting up the TZ structure.
The fitness function depends on the cost of TZ structure as well as the variety of costs related to the environment where the it is to be placed. E.g.: the earthworks, vegetation removal, obstacles avoidance, etc. There are no restrictions on the fitness function definition. It can depend on any variable which can be represented by a two-dimensional map of any property of the environment.
The formulation of the presented method is suited for application of well-established image processing methods which efficiently evaluate candidate solutions on a GPU. As a result, the employed genetic algorithm efficiently probes the search space. The practical applicability of this approach is demonstrated with three case-studies:
1simultaneous paving of a path with congruent units in a hilly environment with trees & bushes and finding the best location for a pier over an existing river;2constructing of a TZ connector spanning over a mountain valley with lakes (where supports can not be placed);3retrofitting of an existing railway station with a large wheelchair TZ ramp of over 10 m elevation while preserving trees and minimizing the earthworks. |
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| AbstractList | •A novel framework for Truss-Z layout optimization is presented.•It is based on image processing, evolutionary algorithm & massive parallelization.•GPU is used for efficient objective function evaluation in the EA.•3 case-studies are studied: Modular path, Mountain pier & Train station retrofitting
Truss-Z (TZ) is an Extremely Modular System for creating skeletal free-form ramps and ramp networks. The TZ structures are comprised of four variations of two types of basic unit subjected to rotation. The two types of units are: R and L being a mirror reflection of each other. This paper presents a novel method based on image processing, evolutionary algorithm and intensive parallelization of multi-objective optimization of TZ layouts.
The algorithm returns a sequence of modules. The result guarantees a TZ connection between two given points (regions) and minimizes the fitness function representing certain costs associated with setting up the TZ structure.
The fitness function depends on the cost of TZ structure as well as the variety of costs related to the environment where the it is to be placed. E.g.: the earthworks, vegetation removal, obstacles avoidance, etc. There are no restrictions on the fitness function definition. It can depend on any variable which can be represented by a two-dimensional map of any property of the environment.
The formulation of the presented method is suited for application of well-established image processing methods which efficiently evaluate candidate solutions on a GPU. As a result, the employed genetic algorithm efficiently probes the search space. The practical applicability of this approach is demonstrated with three case-studies:
1simultaneous paving of a path with congruent units in a hilly environment with trees & bushes and finding the best location for a pier over an existing river;2constructing of a TZ connector spanning over a mountain valley with lakes (where supports can not be placed);3retrofitting of an existing railway station with a large wheelchair TZ ramp of over 10 m elevation while preserving trees and minimizing the earthworks. |
| Author | Szklarski, Jacek Zawidzki, Machi |
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| References | Pratt, Geisow (bib0065) 1986; 6 Wong (bib0090) 2009 Zawidzki, Jankowski (bib0060) 2018; 21 Zawidzki, Nishinari (bib0025) 2012; 47 Gomez-Pulido, Vega-Rodriguez, Sanchez-Perez, Priem-Mendes, Carreira (bib0100) 2011; 12 Fortin, De Rainville, Gardner, Parizeau, Gagné (bib0115) 2012, July; 13 Zawidzki, Nishinari (bib0015) 2013; 17 Zawidzki, Jankowski (bib0050) 2017 Chwiałkowski (bib0120) 2017 Zawidzki (bib0035) 2016; 100 Zawidzki, Nagakura (bib0045) 2014 Soca, Blengio, Pedemonte, Ezzatti (bib0085) 2010, July Nowotniak, Kucharski (bib0080) 2011; 3 Zawidzki (bib0005) 2017 Zawidzki (bib0010) 2013; 8 Zawidzki, Nishinari (bib0030) 2013; 65 Munk, Vio, Steven (bib0070) 2015; 52 Zawidzki, Szklarski (bib0020) 2017 Zawidzki (bib0040) 2015; 81 Wong, Wong (bib0095) 2006 Zawidzki, Jankowski, Szklarski (bib0055) 2017 Gong, Chen, Zhan, Zhang, Li, Zhang, Li (bib0105) 2015; 34 Martínez-Frutos, Herrero-Pérez (bib0110) 2017 Banzhaf, Harding (bib0075) 2009 Wong (10.1016/j.asoc.2018.05.042_bib0095) 2006 Pratt (10.1016/j.asoc.2018.05.042_bib0065) 1986; 6 Wong (10.1016/j.asoc.2018.05.042_bib0090) 2009 Zawidzki (10.1016/j.asoc.2018.05.042_bib0035) 2016; 100 Zawidzki (10.1016/j.asoc.2018.05.042_bib0005) 2017 Zawidzki (10.1016/j.asoc.2018.05.042_bib0020) 2017 Zawidzki (10.1016/j.asoc.2018.05.042_bib0025) 2012; 47 Martínez-Frutos (10.1016/j.asoc.2018.05.042_bib0110) 2017 Zawidzki (10.1016/j.asoc.2018.05.042_bib0015) 2013; 17 Zawidzki (10.1016/j.asoc.2018.05.042_bib0045) 2014 Zawidzki (10.1016/j.asoc.2018.05.042_bib0030) 2013; 65 Zawidzki (10.1016/j.asoc.2018.05.042_bib0055) 2017 Munk (10.1016/j.asoc.2018.05.042_bib0070) 2015; 52 Zawidzki (10.1016/j.asoc.2018.05.042_bib0060) 2018; 21 Gomez-Pulido (10.1016/j.asoc.2018.05.042_bib0100) 2011; 12 Fortin (10.1016/j.asoc.2018.05.042_bib0115) 2012; 13 Zawidzki (10.1016/j.asoc.2018.05.042_bib0050) 2017 Soca (10.1016/j.asoc.2018.05.042_bib0085) 2010 Chwiałkowski (10.1016/j.asoc.2018.05.042_bib0120) 2017 Zawidzki (10.1016/j.asoc.2018.05.042_bib0010) 2013; 8 Banzhaf (10.1016/j.asoc.2018.05.042_bib0075) 2009 Gong (10.1016/j.asoc.2018.05.042_bib0105) 2015; 34 Zawidzki (10.1016/j.asoc.2018.05.042_bib0040) 2015; 81 Nowotniak (10.1016/j.asoc.2018.05.042_bib0080) 2011; 3 |
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