A memetic algorithm and a parallel hyperheuristic island-based model for a 2D packing problem

This work presents several approaches used to deal with the 2D packing problem proposed in the GECCO 2008 contest session. A memetic algorithm, together with the specifically designed local search and variation operators, are presented. A novel parallel model was used to parallelize the approach. The model is a hybrid algorithm which combines a parallel island-based scheme with a hyperheuristic approach. An adaptive behavior is added to the island-based model by applying the hyperheuristic procedure. The main operation of the island-based model is kept, but the configurations of the memetic algorithms executed on each island are dynamically mapped. The model grants more computational resources to those configurations that show a more promising behavior. For this purpose a specific criterion was designed in order to select the configurations with better success expectations. Computational results obtained for the contest problem demonstrate the validity of the proposed model. The best reported solutions for the problem contest instance have been achieved by using the here presented approaches.

[1]  Freda Kemp,et al.  The Handbook of Parametric and Nonparametric Statistical Procedures , 2003 .

[2]  Gara Miranda,et al.  2D Cutting Stock Problem: A New Parallel Algorithm and Bounds , 2007, Euro-Par.

[3]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[4]  Gerhard Wäscher,et al.  An improved typology of cutting and packing problems , 2007, Eur. J. Oper. Res..

[5]  Türkay Dereli,et al.  A Hybrid Simulated-Annealing Algorithm for Two-Dimensional Strip Packing Problem , 2007, ICANNGA.

[6]  Adam Stawowy,et al.  Evolutionary based heuristic for bin packing problem , 2008, Comput. Ind. Eng..

[7]  Enrique Alba,et al.  Parallel Metaheuristics: A New Class of Algorithms , 2005 .

[8]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[9]  Chung-lun Li,et al.  Bin‐packing problem with concave costs of bin utilization , 2006 .

[10]  Maliha S. Nash,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 2001, Technometrics.

[11]  Francisco Luna,et al.  Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms , 2009, EMO.

[12]  L. Darrell Whitley,et al.  An overview of evolutionary algorithms: practical issues and common pitfalls , 2001, Inf. Softw. Technol..

[13]  Colin Reeves,et al.  Hybrid genetic algorithms for bin-packing and related problems , 1996, Ann. Oper. Res..

[14]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Jacek Blazewicz,et al.  A New Parallel Approach for Multi-dimensional Packing Problem , 2001, PPAM.

[16]  Marcus Gallagher,et al.  A hybrid approach to parameter tuning in genetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[17]  V. Zumer,et al.  A metaevolutionary approach for the traveling salesman problem , 2000, ITI 2000. Proceedings of the 22nd International Conference on Information Technology Interfaces (Cat. No.00EX411).

[18]  Deshi Ye,et al.  On-Line Bin Packing with Arbitrary Release Times , 2007, ESCAPE.

[19]  Holger Mauch,et al.  Closest Substring Problem - Results from an Evolutionary Algorithm , 2004, ICONIP.

[20]  Jack Dongarra,et al.  MPI: The Complete Reference , 1996 .

[21]  Gara Miranda,et al.  Metaheuristics for solving a real-world frequency assignment problem in GSM networks , 2008, GECCO '08.

[22]  Harald Dyckhoff,et al.  A typology of cutting and packing problems , 1990 .

[23]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[24]  A. Bagchi,et al.  Best-First Search Methods for Constrained Two-Dimensional Cutting Stock Problems , 1993, Oper. Res..

[25]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[26]  Tzung-Pei Hong,et al.  Two-dimentional Encoding Schema and Genetic Operators , 2006, JCIS.

[27]  Masafumi Hagiwara,et al.  Applying self-adaptive evolutionary algorithms to two-dimensional packing problems using a four corners' heuristic , 2007, Eur. J. Oper. Res..

[28]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[29]  Gara Miranda,et al.  Parallel hyperheuristic: a self-adaptive island-based model for multi-objective optimization , 2008, GECCO '08.

[30]  Holger H. Hoos,et al.  On the Run-time Behaviour of Stochastic Local Search Algorithms for SAT , 1999, AAAI/IAAI.

[31]  Paolo Toth,et al.  Knapsack Problems: Algorithms and Computer Implementations , 1990 .

[32]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[33]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[34]  Edmund K. Burke,et al.  Hyperheuristic Approaches for Multiobjective Optimisation , 2003 .

[35]  Daniele Vigo,et al.  An Exact Approach to the Strip-Packing Problem , 2003, INFORMS J. Comput..