Group influence based improved firefly algorithm for Design Space Exploration of Datapath resource allocation

  • Shathanaa Rajmohan
  • Ramasubramanian Natarajan


Firefly Algorithm which is a recent addition to the evolutionary algorithms, has shown good performance for many multi-objective optimization problems. In this paper, we propose a novel Firefly algorithm for Design Space Exploration of Datapath resource allocation. The Datapath resource allocation problem is NP-Complete and the design space has vast number of design points. To explore the design space in feasible time, the problem is solved using an improved Firefly algorithm. In particular, meeting the constraints presented by different parameters of interest is evaluated as cost based fitness and then solved. The proposed approach modifies Firefly algorithm on four fronts: 1. A new strategy called Group-Influence based attraction, is used for updating fireflies during evolution; 2. To generate diverse and quality initial population, Opposition Based Learning is incorporated to population initialization; 3. In addition to exploration, in order to refine exploitation, Firefly algorithm is hybridized with Tabu search; 4. Tabu search is updated with Lévy flights for finding nearby solutions. The proposed algorithm is compared with other meta-heuristic algorithms with respect to Quality-of-Results and exploration time. Experimental results show that the proposed algorithm outperforms other existing algorithms for standard benchmark instances.


Firefly algorithm Group-influence Datapath resource allocation Design space exploration 


  1. 1.
    Das I (1999) A preference ordering among various Pareto optimal alternatives. Struct Multidiscip Optim 18(1):30–35CrossRefGoogle Scholar
  2. 2.
    Liu HY, Carloni LP (2013) On learning-based methods for design-space exploration with high-level synthesis. In: Proceedings of the 50th Annual Design Automation Conference, pp 1–7Google Scholar
  3. 3.
    Zuluaga M et al (2013) Active learning for multi-objective optimization. In: Proceedings of 30th Int. Conf. on Machine Learning (ICML), pp 462–470Google Scholar
  4. 4.
    Meng P, Althoff A, Gautier Q, Kastner R (2016) Adaptive threshold non-Pareto elimination: re-thinking machine learning for system level design space exploration on FPGAs. In: Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), pp 918–923Google Scholar
  5. 5.
    Piccolboni L, Mantovani P, Guglielmo G, Carloni L (2013) COSMOS: coordination of high-level synthesis and memory optimization for hardware accelerators. ACM Trans Embed Comput Syst 16:1–22. CrossRefGoogle Scholar
  6. 6.
    Ascia G, Catania V, Palesi M (2002) An evolutionary approach for pareto-optimal configurations in SoC platforms. In: SoC Design Methodologies. Springer, Boston, pp 157–168CrossRefGoogle Scholar
  7. 7.
    Yessin G, Badawy AHA, Narayana V, Mayhew D, Ghazawi TE (2014) "CERE": A CachE Recommendation Engine: Efficient Evolutionary Cache Hierarchy Design Space Exploration. In: IEEE Int. Conf. on High Performance Computing and Communications, pp 566–573Google Scholar
  8. 8.
    Ascia G, Catania V, Di Nuovo AG, Palesi M, Patti D (2011) Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems. Applied Soft Computing 11:382–398CrossRefGoogle Scholar
  9. 9.
    Krishnan V, Katkoori S (2006) A genetic algorithm for the design space exploration of datapaths during high-level synthesis. IEEE Trans Evol Comput 10:213–229CrossRefGoogle Scholar
  10. 10.
    Badawy AH, Yassin G, Narayana V, Mayhew D, El-Ghazawi T (2017) Optimizing thin client caches for mobile cloud computing. Concurrency Computat: Pract Exper 29.
  11. 11.
    Mishra VK, Sengupta A (2014) MO-PSE: adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design. Adv Eng Softw 67:111–124Google Scholar
  12. 12.
    Bhuvaneswari MC, Harish Ram DS, Neelaveni R (2015) Design space exploration for scheduling and allocation in high level synthesis of Datapaths. In: Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems, 1st edn. Springer, IndiaGoogle Scholar
  13. 13.
    Bhadauria S, Sengupta A (2015) Adaptive bacterial foraging driven datapath optimization: exploring power-performance tradeoff in high level synthesis. Appl Math Comput 269:265–278. MathSciNetCrossRefGoogle Scholar
  14. 14.
    Camposano R (1991) Path-based scheduling for synthesis. IEEE Trans Comput-Aided Des 10:85–93Google Scholar
  15. 15.
    Carrion Schafer B (2016) Probabilistic multiknob high-level synthesis design space exploration acceleration. IEEE Trans Comput Aided Des Integr Circuits Syst 35(3):394–406CrossRefGoogle Scholar
  16. 16.
    da Silva JS, Bampi S (2015) Area-oriented iterative method for design space exploration with high-level synthesis. In: Proceedings of 6th Latin American Symposium on Circuits & Systems (LASCAS), pp 1–4Google Scholar
  17. 17.
    Schafer BC (2015) Hierarchical high-level synthesis design space exploration with incremental exploration support. IEEE Embedded Syst Lett 7:51–54CrossRefGoogle Scholar
  18. 18.
    Jui-Ming C, Massoud P (1997) Energy minimization using multiple supply voltages. IEEE Trans Very Large Scale Integr (VLSI) Syst 5:436–443CrossRefGoogle Scholar
  19. 19.
    Yang XS (2009) Firefly algorithms for multimodal optimization. In: Proceedings of the International symposium on stochastic algorithms. Springer, Berlin, pp 169–178Google Scholar
  20. 20.
    Yang XS (2010) Firefly algorithm, Lévy flights and global optimization. In: Research and development in intelligent systems XXVI. Springer, London, pp 209–218CrossRefGoogle Scholar
  21. 21.
    Apostolopoulos T, Vlachos A (2011) Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int J Combin 2011:1–23MathSciNetCrossRefGoogle Scholar
  22. 22.
    Fister I, Fister I Jr, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46CrossRefGoogle Scholar
  23. 23.
    Fister I, Yang XS, Fister D, Fister I Jr (2014) Firefly algorithm: a brief review of the expanding literature. In: Yang XS (ed) Cuckoo search and firefly algorithm. Springer, New York, pp 347–360CrossRefGoogle Scholar
  24. 24.
    Fister IJ, Perc M, Kamal SM, Fister I (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Math Comput 252:155–165MathSciNetzbMATHGoogle Scholar
  25. 25.
    Chandrasekaran K, Simon SP, Padhy NP (2013) Binary real coded firefly algorithm for solving unit commitment problem. Inf Sci 249:67–84. CrossRefGoogle Scholar
  26. 26.
    dos Santos Coelho L, de Andrade Bernert DL, Mariani VC (2011) A chaotic firefly algorithm applied to reliability-redundancy optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp 89–98Google Scholar
  27. 27.
    Gandomi A, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98MathSciNetCrossRefGoogle Scholar
  28. 28.
    Husselmann AV, Hawick KA (2012) Parallel parametric optimisation with firefly algorithms on graphical processing units. In: Technical, Report CSTN-141, pp 77–83Google Scholar
  29. 29.
    Liu G (2013) A multipopulation firefly algorithm for correlated data routing in underwater wireless sensor networks. Int J Distrib Sens Netw 9:865154. CrossRefGoogle Scholar
  30. 30.
    Adaniya MHAC et al (2013) Anomaly detection using metaheuristic firefly harmonic clustering. J Netw 8(1):82–91Google Scholar
  31. 31.
    Osaba E, Yang XS, Diaz F, Onieva E, Masegosa AD, Perallos A (2016) A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comput 21:5295–5308. CrossRefGoogle Scholar
  32. 32.
    Luthra J, Pal SK (2011) A hybrid firefly algorithm using genetic operators for the cryptanalysis of a monoalphabetic substitution cipher. In: Proceedings of World Congress on Information and Communication Technologies (WICT), pp 202–206Google Scholar
  33. 33.
    Abdullah A, Deris S, Anwar S, Arjunan SNV (2013) An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters. PLoS One 8:e56310. CrossRefGoogle Scholar
  34. 34.
    Srivastava A, Chakrabarti S, Das S, Ghosh S, Jayaraman VK (2013) Hybrid firefly based simultaneous gene selection and cancer classification using support vector machines and random forests. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012), pp 485–494CrossRefGoogle Scholar
  35. 35.
    Hassanzadeh T, Meybodi MR (2012) A new hybrid algorithm based on firefly algorithm and cellular learning automata. In: Proceedings of 20th Iranian Conference on Electrical Engineering, pp 628–633Google Scholar
  36. 36.
    Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of International conference on computational intelligence for modelling control and automation IEEE, pp 695–701Google Scholar
  37. 37.
    Wang H, Wu Z, Rahnamayan S (2011) Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft Comput 15(11):2127–2140CrossRefGoogle Scholar
  38. 38.
    Rahnamayan S, Tizhoosh HR, Salama M (2008) Opposition-based differential evolution. Evol Comput IEEE Trans 12(1):64–79CrossRefGoogle Scholar
  39. 39.
    Li F, Morgan R, Williams D (1997) Hybrid genetic approaches to ramping rate constrained dynamic economic dispatch. Electr Power Syst Res 43(11):97–103CrossRefGoogle Scholar
  40. 40.
    Lo CC, Chang WH (2000) A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem. IEEE Trans Syst Man Cybern, Part B, Cybern 30(3):461–470CrossRefGoogle Scholar
  41. 41.
    Somasundaram P, Lakshmiramanan R, Kuppusamy K (2005) Hybrid algorithm based on EP and LP for security constrained economic dispatch problem. Electr Power Syst Res 76(1–3):77–85CrossRefGoogle Scholar
  42. 42.
    Tseng LY, Liang SC (2005) A hybrid metaheuristic for the quadratic assignment problem. Comput Optim Appl 34(1):85–113MathSciNetCrossRefGoogle Scholar
  43. 43.
    Sinha A, Goldberg DE (2003) A Survey of hybrid genetic and evolutionary algorithms, IlliGAL Report No. 2002XXX, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, ILGoogle Scholar
  44. 44.
    Krasnogor N, Aragón A, Pacheco J (2006) Memetic algorithms. In: Metaheuristic procedures for training neutral networks. Springer, Boston, pp 225–248CrossRefGoogle Scholar
  45. 45.
    Aruldoss AVT, Ebenezer JA (2005) A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect. Int J Electr Power Energy Syst 27(8):594–601CrossRefGoogle Scholar
  46. 46.
    Burke EK, Smith AJ (2000) Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Trans Power Syst 1(1):122–128CrossRefGoogle Scholar
  47. 47.
    Merz P (2000) Memetic algorithms for combinatorial optimization problems: fitness landscapes and efective search strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, GermanyGoogle Scholar
  48. 48.
    Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13: 533–549Google Scholar
  49. 49.
    Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, NorwellCrossRefGoogle Scholar
  50. 50.
    Viswanathan G et al (2002) Lévy flight random searches in biological phenomena. Physica A 314:208–213MathSciNetCrossRefGoogle Scholar
  51. 51.
    Shlesinger MF (2006) Search research. Nature 443:281–282 Accessed 5 Oct 2018
  52. 52.
    Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226:1830–1844MathSciNetCrossRefGoogle Scholar
  53. 53.
    Express Benchmark Suite. (2014) University of California, Santa Barbara. Accessed 13 Aug 2018
  54. 54.
    Schafer BC, Mahapatra A (2014) S2CBench: synthesizable SystemC benchmark suite for high-level synthesis. IEEE Embedded Syst Lett 6:53–56CrossRefGoogle Scholar
  55. 55.
    Reynders N, Dehaene W (2011) A 190 mV supply 10 MHz 90 nm CMOS pipelined sub-threshold adder using variation-resilient circuit techniques. In: Proceedings of Asian Solid State Circuits Conference (A-SSCC), pp 113–116Google Scholar
  56. 56.
    Shrestha R, Rastogim U (2016) Design and implementation of area-efficient and low-power configurable booth-multiplier. In: Proceedings of Int. Conf. on VLSI Design and Int. Conf. on Embedded Systems, pp 599–600Google Scholar
  57. 57.
    Chang SK, Wey CL (2012) A fast 64-bit hybrid adder design in 90 nm CMOS process. In: Proceedings of IEEE Midwest Symp. on Circuits and Systems, pp 414–417Google Scholar
  58. 58.
    Shuhao Y, Shenglong Z, Yan M, Demei M (2015) A variable step size firefly algorithm for numerical optimization. Appl Math Comput 263:214–220. MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Wang H, Zhou X, Sun H, Yu X, Zhao J, Zhang H, Cui L (2017) Firefly algorithm with adaptive control parameters. Soft Comput 21:5091–5102CrossRefGoogle Scholar
  60. 60.
    Wang H, Cui Z, Sun H, Rahnamayan S, Yang XS (2017) Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Comput 21:5325–5339CrossRefGoogle Scholar
  61. 61.
    Gou J, Lei YX, Guo WP, Wang C, Cai YQ, Luo W (2017) A novel improved particle swarm optimization algorithm based on individual difference evolution. Appl Soft Comput 57:468–481CrossRefGoogle Scholar
  62. 62.
    Cheng J, Wang L, Jiang Q, Xiong Y (2018) A novel cuckoo search algorithm with multiple update rules. Appl Intell 48:4192–4211. CrossRefGoogle Scholar
  63. 63.
    Zheng S, Janecek A, Tan Y (2013) Enhanced fireworks algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp 2069–2077Google Scholar
  64. 64.
    García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044–2064CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringNational Institute of TechnologyTiruchirappalliIndia

Personalised recommendations