Towards Multi-objective Optimization of Automatic Design Space Exploration for Computer Architecture through Hyper-heuristic


  • M. Latif Department of Software Engineering, NED University of Engineering & Technology, Pakistan
  • M. A. Ismail Department of Computer and Information Systems Engineering, NED University of Engineering & Technology, Pakistan


Multi-objective optimization is an NP-hard problem. ADSE (automatic design space exploration) using heuristics has been proved to be an appropriate method in resolving this problem. This paper presents a hyper-heuristic technique to solve the DSE issue in computer architecture. Two algorithms are proposed. A hyper-heuristic layer has been added to the FADSE (framework for automatic design space exploration) and relevant algorithms have been implemented. The benefits of already existing multi-objective algorithms have been joined in order to strengthen the proposed algorithms. The proposed algorithms, namely RRSNS (round-robin scheduling NSGA-II and SPEA2) and RSNS (random scheduling NSGA-II and SPEA2) have been evaluated for the ADSE problem. The results have been compared with NSGA-II and SPEA2 algorithms. Results show that the proposed methodologies give competitive outcomes in comparison with NSGA-II and SPEA2.


hyper-heuristic, multi-objective optimization, design space exploration, x86 processor


Download data is not yet available.


E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, J. R. Woodward, “A Classification of Hyper-Heuristic Approaches: Revisited”, in: Handbook of Metaheuristics, pp. 453-477, Springer, 2019 DOI:

S. S. Choong, L. P. Wong, C. P. Lim, “Automatic design of hyper-heuristic based on reinforcement learning”, Information Sciences, Vol. 436-437, pp. 89-107, 2018 DOI:

L. Vintan, R. Chis, M. A. Ismail, C. Cotofana, “Improving computing systems automatic multiobjective optimization through meta-optimization”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 35, No. 7, pp. 1125-1129, 2016 DOI:

W. G. Jackson, E. Ozcan, J. H. Drake, “Late Acceptance-Based Selection Hyper-Heuristics for Cross-Domain Heuristic Search”, 13th UK Workshop on Computational Intelligence, Guildford, UK, September 9-11, 2013 DOI:

J. H. Drake, E. Ozcan, E. K. Burke, “A Modified Choice Function Hyper-Heuristic Controlling Unary and Binary Operators”, IEEE Congress on Evolutionary Computation, Sendai, Japan, May 25-28, 2015 DOI:

H. A. Calborean, Multi-Objective Optimization of Advanced Computer Architectures Using Domain-Knowledge, PhD Thesis, University of Sibiu, 2011

V. Zaccaria, G. Palermo, F. Castro, C. Silvano, G. Mariani, “Multicube Explorer: An Open Source Framework for Design Space Exploration of Chip Multi-Processors”, 23th International Conference on Architecture of Computing Systems 2010, Hannover, Germany, February 22-23, 2010

C. Silvano, W. Fornaciari, G. Palermo, V. Zaccaria, F. Castro, M. Martinez, S. Bocchio, R. Zafalon, P. Avasare, G. Vanmeerbeeck, C. Ykman-Couvreur, M. Wouters, C. Kavka, L. Onesti, A. Turco, U. Bondik, G. Mariani, H. Posadas, E. Villar, C. Wu, F. Dongrui, Z. Hao, T. Shibin, “Multicube: Multi-Objective Design Space Exploration of Multi-Core Architectures”, IEEE Computer Society Annual Symposium on VLSI, Kefalonia, Greece, July 5-7, 2010 DOI:

Z. J. Jia, A. D. Pimentel, M. Thompson, T. Bautista, A. Nunez, “NASA: A Generic Infrastructure for System-Level MP-SoC Design Space Exploration”, 8th IEEE Workshop on Embedded Systems for Real-Time Multimedia, Scottsdale, USA, October 28-29, 2010 DOI:

J. J. Durillo, A. J. Nebro, “jMetal: A java framework for multi-objective optimization”, Advances in Engineering Software, Vol. 42, No. 10, pp. 760-771, 2011 DOI:

E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, TIK-Report, Vol. 103, Swiss Federal Institute of Technology, 2001

P. Cowling, G. Kendall, E. Soubeiga, “A Hyperheuristic Approach to Scheduling a Sales Summit”, in: Lecture Notes in Computer Science, Vol. 2079, Springer, 2001 DOI:

R. Bai, J. Blazewicz, E. K. Burke, G. Kendall, B. McCollum, “A simulated annealing hyper-heuristic methodology for flexible decision support”, 4OR, Vol. 10, No. 1, pp. 43-66, 2012 DOI:

B. Kiraz, A. S. Etaner-Uyar, E. Ozcan, “Selection hyper-heuristics in dynamic environments”, Journal of the Operational Research Society, Vol. 64, No. 12, pp. 1753-1769, 2013 DOI:

R. Chis, A. Florea, C. Buduleci, L. Vintan, “Multi-objective optimization for an enhanced multi-core SNIPER simulator”, Proceedings of the Romanian Academy-Series A, Vol. 19, No. 1, pp. 85-93, 2018

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, pp. 182-197, 2002 DOI:

A. Kheiri, E. Ozcan, “A Hyper-Heuristic with a Round Robin Neighbourhood Selection”, in: Lecture Notes in Computer Science, Vol. 7832, Springer, 2013 DOI:

S. C. Woo, M. Ohara, E. Torrie, J. P. Singh, A. Gupta, “The SPLASH-2 Programs: Characterization and Methodological Considerations”, 22nd Annual International Symposium on Computer Architecture, Santa Margherita Ligure, Italy, June 22-24, 1995 DOI:

C. A. C. Coello, G. B. Lamont, D. A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2007

E. Zitzler, L. Thiele, “Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach”, IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4, pp. 257-271, 1999 DOI:


How to Cite

M. Latif and M. A. Ismail, “Towards Multi-objective Optimization of Automatic Design Space Exploration for Computer Architecture through Hyper-heuristic”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 3, pp. 4292–4297, Jun. 2019.


Abstract Views: 594
PDF Downloads: 352

Metrics Information

Most read articles by the same author(s)