Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Hybrid heuristic algorithms for the multi objective load balancing of 2D bin packing problems
Date
2015-09-23
Author
Muhammet, Beyaz
Dökeroğlu, Tansel
Coşar, Ahmet
Metadata
Show full item record
Item Usage Stats
187
views
0
downloads
Cite This
2D Bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem. Multiobjective versions of this well-known industrial engineering problem can occur frequently in real world application. Recently, Hybrid Evolutionary Algorithms have appear as a new area of research with their ability to combine alternative heuristics and local search mechanisms together for higher quality solutions. In this study, we propose a set of novel multiobjective hybrid genetic and memetic algorithms that make use of the state-of-the-art metaheuristics and local search techniques for minimizing the number of bins while also maintaining the load balance. We analyze the optimization time and the resulting solution quality of the proposed algorithms on an offline 2DBPP benchmark problem set with 500 instances. Using these results of exhaustive experiments, we conclude that the proposed hybrid algorithms are robust with their ability to obtain a high percentage of the optimal solutions.
Subject Keywords
Genetic algorithm
,
Load balance
,
Memetic algorithm
,
Multiobjective genetic algorithm
,
Swap mutation
URI
http://link.springer.com/book/10.1007/978-3-319-22635-4
https://hdl.handle.net/11511/78237
Conference Name
30th International Symposium on Computer and Information Sciences, (23 - 25 Eylül 2015)
Collections
Graduate School of Natural and Applied Sciences, Conference / Seminar
Suggestions
OpenMETU
Core
Hybrid metaheuristic algorithms for single and multi-objective 2d bin packing problem
Beyaz, Muhammed; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2015)
2D Bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem. Objects with di erent width and length sizes are packed in order to minimize the number of unit-capacity bins according to an objective function. Single or multiobjective versions of this well-known industrial engineering problem can be faced frequently in real life situations. There have been several heuristics proposed for the solution of 2DBPP until now where it is not possible to find the exact solutions for large problem i...
Optimization of one-dimensional Bin Packing Problem with island parallel grouping genetic algorithms
Dokeroglu, Tansel; Coşar, Ahmet (2014-09-01)
The well-known one-dimensional Bin Packing Problem (BPP) of whose variants arise in many real life situations is a challenging NP-Hard combinatorial optimization problem. Metaheuristics are widely used optimization tools to find (near-) optimal solutions for solving large problem instances of BPP in reasonable running times. With this study, we propose a set of robust and scalable hybrid parallel algorithms that take advantage of parallel computation techniques, evolutionary grouping genetic metaheuristics,...
Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems
Beyaz, Muhammed; Dokeroglu, Tansel; Coşar, Ahmet (2015-11-01)
The offline 2D bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem in which objects with various width and length sizes are packed into minimized number of 2D bins. Various versions of this well-known industrial engineering problem can be faced frequently. Several heuristics have been proposed for the solution of 2DBPP but it has not been possible to find the exact solutions for large problem instances. Next fit, first fit, best fit, unified tabu search, genetic and memetic algorith...
Minimum-weight spanning tree algorithms - A survey and empirical study
Bazlamaçcı, Cüneyt Fehmi (2001-07-01)
The minimum-weight spanning tree problem is one of the most typical and well-known problems of combinatorial optimisation. Efficient solution techniques had been known for many years. However, in the last two decades asymptotically faster algorithms have been invented. Each new algorithm brought the time bound one step closer to linearity and finally Karger, Klein and Tarjan proposed the only known expected linear-time method. Modern algorithms make use of more advanced data structures and appear to be more...
Optimization of the array geometry for direction finding
Özaydın, Seval; Koç, Seyit Sencer; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2003)
In this thesis, optimization of the geometry of non-uniform arrays for direction finding yielding unambiguous results is studied. A measure of similarity between the array response vectors is defined. In this measure, the effects of antenna array geometry, source placements and antenna gains are included as variable parameters. Then, assuming that the antenna gains are known and constant, constraints on the similarity function are developed and described to result in unambiguous configurations and maximum r...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Muhammet, T. Dökeroğlu, and A. Coşar, “Hybrid heuristic algorithms for the multi objective load balancing of 2D bin packing problems,” presented at the 30th International Symposium on Computer and Information Sciences, (23 - 25 Eylül 2015), London, UK, 2015, Accessed: 00, 2021. [Online]. Available: http://link.springer.com/book/10.1007/978-3-319-22635-4.