International Journal of Parallel Programming

, Volume 42, Issue 5, pp 681–709 | Cite as

Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms

  • Pavel Krömer
  • Jan Platoš
  • Václav Snášel


Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.


Graphic processing units Genetic algorithms Differential evolution Particle swarm optimization Simulated annealing Survey 



This work was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070) and by the Bio-Inspired Methods: research, development and knowledge transfer project, Reg. No. CZ.1.07/2.3. 00/20.0073 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic.


  1. 1.
    Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20(1), 1–48 (2013). doi: 10.1111/j.1475-3995.2012.00862.x CrossRefMATHGoogle Scholar
  2. 2.
    Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithms. Complexity 4(4), 31–52 (1999)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Arabas, J., Maitre, O., Collet, P.: PARADE: a massively parallel differential evolution template for EASEA. In: Proceedings of the 2012 International Conference on Swarm and Evolutionary Computation, SIDE’12, pp. 12–20. Springer, Berlin (2012). doi: 10.1007/978-3-642-29353-5_2
  4. 4.
    Arenas, M.G., Romero, G., Mora, A.M., Castillo, P.A., Merelo, J.J.: GPU parallel computation in bioinspired algorithms: a review. In: Advances in Intelligent Modelling and Simulation, Studies in Computational Intelligence, vol. 422, pp. 113–134. Springer (2012)Google Scholar
  5. 5.
    Bacardit, J., Llora, X.: Large-scale data mining using genetics-based machine learning. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(1), 37–61 (2013)CrossRefGoogle Scholar
  6. 6.
    Bajrami, E., Asic, M., Cogo, E., Trnka, D., Nosovic, N.: Performance comparison of simulated annealing algorithm execution on GPU and CPU. In: MIPRO, 2012 Proceedings of the 35th International Convention, pp. 1785–1788 (2012)Google Scholar
  7. 7.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming—An Introduction on the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, San Francisco, CA (1998)MATHGoogle Scholar
  8. 8.
    Banerjee, P., Jones, M., Sargent, J.: Parallel simulated annealing algorithms for cell placement on hypercube multiprocessors. Parallel Distrib. Syst. IEEE Trans. 1(1), 91–106 (1990). doi: 10.1109/71.80128 CrossRefGoogle Scholar
  9. 9.
    Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Stat. Sci. 8(1), 10–15 (1993)CrossRefGoogle Scholar
  10. 10.
    Bessis, N., Sotiriadis, S., Cristea, V., Pop, F.: Modelling requirements for enabling meta-scheduling in inter-clouds and inter-enterprises. In: Intelligent Networking and Collaborative Systems (INCoS), 2011 Third International Conference on, pp. 149–156 (2011). doi: 10.1109/INCoS.2011.120
  11. 11.
    Bessis, N., Sotiriadis, S., Xhafa, F., Pop, F., Cristea, V.: Meta-scheduling issues in interoperable hpcs, grids and clouds. Int. J. Web Grid Serv. 8(2), 153–172 (2012). doi: 10.1504/IJWGS.2012.048403 CrossRefGoogle Scholar
  12. 12.
    Buchty, R., Heuveline, V., Karl, W., Weiss, J.P.: A survey on hardware-aware and heterogeneous computing on multicore processors and accelerators. Concurr. Comput. Pract. Exp. 24(7), 663–675 (2012). doi: 10.1002/cpe.1904 CrossRefGoogle Scholar
  13. 13.
    Cagnoni, S., Bacchini, A., Mussi, L.: OpenCL implementation of particle swarm optimization: a comparison between multi-core CPU and GPU performances. In: Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., Vega, F., Caro, G., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A., Farooq, M., Langdon, W., Merelo-Guervós, J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A., Togelius, J., Urquhart, N., Uyar, A., Yannakakis, G. (eds.) Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 7248, pp. 406–415. Springer, Berlin (2012). doi: 10.1007/978-3-642-29178-4_41.
  14. 14.
    Cano, A., Zafra, A., Ventura, S.: Speeding up the evaluation phase of GP classification algorithms on GPUs. Soft Comput. 16(2), 187–202 (2012)CrossRefGoogle Scholar
  15. 15.
    Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer, Dordrecht (2000)MATHGoogle Scholar
  16. 16.
    Cavuoti, S., Garofalo, M., Brescia, M., Pescap, A., Longo, G., Ventre, G.: Genetic algorithm modeling with GPU parallel computing technology. In: Neural Nets and Surroundings, Smart Innovation, Systems and Technologies, vol. 19, pp. 29–39. Springer (2013)Google Scholar
  17. 17.
    Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45, 41–51 (1985). doi: 10.1007/BF00940812 CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    Chen, H., Flann, N., Watson, D.: Parallel genetic simulated annealing: a massively parallel SIMD algorithm. Parallel Distrib. Syst. IEEE Trans. 9(2), 126–136 (1998). doi: 10.1109/71.663870 CrossRefGoogle Scholar
  19. 19.
    Chen, R.B., Hsieh, D.N., Hung, Y., Wang, W.: Optimizing latin hypercube designs by particle swarm. Stat. Comput., 1–14 (2012). doi: 10.1007/s11222-012-9363-3
  20. 20.
    Cheang, S.M., Leung, K.S., Lee, K.H.: Genetic parallel programming: design and implementation. Evolut. Comput. 14(2), 129–156 (2006)CrossRefGoogle Scholar
  21. 21.
    Chitty, D. M., Malvern, Q.: A data parallel approach to genetic programming using programmable graphics hardware. In: GECCO G07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1566–1573, ACM Press (2007)Google Scholar
  22. 22.
    Chitty, D.: Fast parallel genetic programming: Multi-core CPU versus many-core GPU. Soft Comput. 16(10), 1795–1814 (2012)CrossRefGoogle Scholar
  23. 23.
    Choong, A., Beidas, R., Zhu, J.: Parallelizing simulated annealing-based placement using gpgpu. In: Field Programmable Logic and Applications (FPL), 2010 International Conference on, pp. 31–34 (2010). doi: 10.1109/FPL.2010.17
  24. 24.
    Clerc, M.: Particle Swarm Optimization. ISTE. Wiley (2010).
  25. 25.
    Czarn, A., MacNish, C., Vijayan, K., Turlach, B.A.: Statistical exploratory analysis of genetic algorithms: the influence of gray codes upon the difficulty of a problem. In: Webb, G.I., Yu, X. (ed.) Australian Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 3339, pp. 1246–1252. Springer (2004)Google Scholar
  26. 26.
    Datta, D., Mehta, S., Shalivahan, Srivastava, R.: Recent Advances in Information Technology (RAIT), 2012 1st International Conference on CUDA based Particle Swarm Optimization for geophysical inversion, pp. 416–420 (2012). doi: 10.1109/RAIT.2012.6194456
  27. 27.
    de Veronese, L., Krohling, R.: Differential evolution algorithm on the GPU with C-CUDA. In: Evolutionary Computation (CEC), 2010 IEEE Congress on, pp. 1–7 (2010). doi: 10.1109/CEC.2010.5586219
  28. 28.
    Desell, T.J., Anderson, D.P., Magdon-Ismail, M., Newberg, H.J., Szymanski, B.K., Varela, C.A.: An analysis of massively distributed evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)Google Scholar
  29. 29.
    Engelbrecht, A.: Computational Intelligence: An Introduction, 2nd edn. Wiley, New York, NY (2007)CrossRefGoogle Scholar
  30. 30.
    Fabris, F., Krohling, R.A.: A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA. Expert Syst. Appl. 39(12), 10,324–10,333 (2012). doi: 10.1016/j.eswa.2011.10.015,
  31. 31.
    Ferreiro, A., García, J., López-Salas, J., Vázquez, C.: An efficient implementation of parallel simulated annealing algorithm in GPUs. J. Glob. Optim., 1–28 (2012). doi: 10.1007/s10898-012-9979-z
  32. 32.
    Franco, M.A., Krasnogor, N., Bacardit, J.: Speeding up the evaluation of evolutionary learning systems using GPGPUs. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO10, pp. 1039–1046. ACM, New York, NY (2010)Google Scholar
  33. 33.
    Frishman, Y., Tal, A.: Multi-level graph layout on the GPU. IEEE Trans. Vis. Comput. Graphics 13(6), 1310–1319 (2007). doi: 10.1109/TVCG.2007.70580 CrossRefGoogle Scholar
  34. 34.
    Gallego, R., Alves, A., Monticelli, A., Romero, R.: Parallel simulated annealing applied to long term transmission network expansion planning. Power Syst. IEEE Trans. 12(1), 181–188 (1997). doi: 10.1109/59.574938 CrossRefGoogle Scholar
  35. 35.
    General-purpose computation on graphics hardware. Accessed Jul 2013
  36. 36.
    Hager, G., Zeiser, T., Wellein, G.: Data access optimizations for highly threaded multi-core cpus with multiple memory controllers. In: Parallel and Distributed Processing. IPDPS 2008. IEEE International Symposium on, pp. 1–7 (2008). doi: 10.1109/IPDPS.2008.4536341
  37. 37.
    Han, Y., Roy, S., Chakraborty, K.: Optimizing simulated annealing on gpu: a case study with ic floorplanning. In: Quality Electronic Design (ISQED), 2011 12th International Symposium on, pp. 1–7 (2011). doi: 10.1109/ISQED.2011.5770735
  38. 38.
    Harding, S., Banzhaf, W.: Fast genetic programming on GPUs. Genet. Program. 4445(3), 90–101 (2007)CrossRefGoogle Scholar
  39. 39.
    Harding, S.: Genetic Programming on Graphics Processing Units Bibliography. Accessed Jul 2013
  40. 40.
    Hofmann, J., Limmer, S., Fey, D.: Performance investigations of genetic algorithms on graphics cards. Swarm Evolut. Comput. 12, 33–47 (2013)Google Scholar
  41. 41.
    Hung, Y., Wang, W.: Accelerating parallel particle swarm optimization via GPU. Optim. Methods Softw. 27(1), 33–51 (2012)CrossRefMATHMathSciNetGoogle Scholar
  42. 42.
    Hwu, W.W.: Illinois ECE 498AL: programming massively parallel processors. In: Lecture 13: Reductions and Their Implementation. (2009)
  43. 43.
    Jaroš, J.: Jiri Jaros’s software website, Accessed Jul 2013
  44. 44.
    Jaroš, J., Pospíchal, P.: A fair comparison of modern CPUs and GPUs running the genetic algorithm under the knapsack benchmark. In: Di Chio, C. et al. (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, pp. 426–435. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29178-4_43
  45. 45.
    Jaroš, J.: Multi-GPU island-based genetic algorithm for solving the knapsack problem. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)Google Scholar
  46. 46.
    Jayaraman, R., Darema, F.: Error tolerance in parallel simulated annealing techniques. In: Computer Design: VLSI in Computers and Processors. ICCD ’88., Proceedings of the 1988 IEEE International Conference on, pp. 545–548 (1988). doi: 10.1109/ICCD.1988.25759
  47. 47.
    Juillé, H., Pollack, J.B.: Massively parallel genetic programming. In: Advances in Genetic Programming vol. 2, chapter 17, pp. 339–358. MIT Press (1996)Google Scholar
  48. 48.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Neural Networks, Proceedings., IEEE International Conference on, vol. 4, pp. 1942–1948 (1995). doi: 10.1109/ICNN.1995.488968
  49. 49.
    Kilic, O., El-Araby, E., Nguyen, Q., Dang, V.: Bio-inspired optimization for electromagnetic structure design using full-wave techniques on GPUs. In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, pp. n/a–n/a (2013). doi: 10.1002/jnm.1878
  50. 50.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by aimulated annealing. Science 220(4598), 671–680 (1983). doi: 10.1126/science.220.4598.671 CrossRefMATHMathSciNetGoogle Scholar
  51. 51.
    Krömer, P., Platoš, J., Snášel, V.: Differential evolution for the linear ordering problem implemented on CUDA. In: Smith, A.E. (ed.) Proceedings of the 2011 IEEE Congress on Evolutionary Computation, pp. 790–796. IEEE Computational Intelligence Society, IEEE Press, New Orleans, USA (2011)Google Scholar
  52. 52.
    Krömer, P., Platoš, J., Snášel, V.: A brief survey of differential evolution on graphic processing Units. In: IEEE Symposium on Differential Evolution (SDE), pp. 157–164 (2013)Google Scholar
  53. 53.
    Krömer, P., Snášel, V., Platoš, J., Abraham, A.: Many-threaded implementation of Differential Evolution for the CUDA platform. In: Krasnogor, N., Lanzi, P.L. (ed.) GECCO, pp. 1595–1602. ACM (2011)Google Scholar
  54. 54.
    Langdon, W.B., Harrison, A.P.: GP on SPMD parallel graphics hardware for mega bioinformatics data mining. Soft Comput. 12(12), 1169–1183 (2008)CrossRefGoogle Scholar
  55. 55.
    Langdon, W.B.: Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In: Cantu-Paz, E., de Vega, F. (ed.). Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, pp. 113–141. Springer, Berlin (2010)Google Scholar
  56. 56.
    Langdon, W.B.: Graphics processing units and genetic programming: an overview. Soft Comput. 15, 1657–1669 (2011). doi: 10.1007/s00500-011-0695-2 CrossRefGoogle Scholar
  57. 57.
    Leskinen, J., Périaux, J.: Distributed evolutionary optimization using Nash games and GPUs–applications to CFD design problems. Comput. Fluids (0) (2012). doi: 10.1016/j.compfluid.2012.03.017,
  58. 58.
    Li, H., Liu, C.: Prediction of protein structures using GPU based simulated annealing. In: Machine Learning and Applications (ICMLA), 2012 11th International Conference on, vol. 1, pp. 630–633 (2012). doi: 10.1109/ICMLA.2012.117
  59. 59.
    Luong, T., Melab, N., Talbi, E.-G.: GPU-based island model for evolutionary algorithms. In: GECCO’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1089–1096. ACM, New York (2010)Google Scholar
  60. 60.
    Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing: a genetic algorithm. Parallel Comput. 21(1), 1–28 (1995). doi: 10.1016/0167-8191(94)00071-H CrossRefMATHMathSciNetGoogle Scholar
  61. 61.
    Maitre, O., Baumes, L.A., Lachiche, N., Corma, A., Collet, P.: Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO’09, pp. 1403–1410. ACM, New York, NY (2009)Google Scholar
  62. 62.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA (1996)Google Scholar
  63. 63.
    Munawar, A., Wahib, M., Munetomo, M., Akama, K.: Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework. Genet. Programm. Evolvable Mach. 10, 391–415 (2009)CrossRefGoogle Scholar
  64. 64.
    Nashed, Y.S., Ugolotti, R., Mesejo, P., Cagnoni, S.: libCudaOptimize: an open source library of GPU-based metaheuristics. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion ’12, pp. 117–124. ACM, New York, NY (2012). doi: 10.1145/2330784.2330803.
  65. 65.
    Nashed, Y.S.G., Mesejo, P., Ugolotti, R., Dubois-Lacoste, J., Cagnoni, S.: A comparative study of three GPU-based metaheuristics. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature—Volume Part II, PPSN’12, pp. 398–407. Springer, Berlin (2012). doi: 10.1007/978-3-642-32964-7_40
  66. 66.
    Nobile, M., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: A gpu-based multi-swarm pso method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Giacobini, M., Vanneschi, L., Bush, W. (eds.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Lecture Notes in Computer Science, vol. 7246, pp. 74–85. Springer, Berlin (2012). doi: 10.1007/978-3-642-29066-4_7.
  67. 67.
    Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running o GPUs. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion ’12, pp. 1421–1422. ACM, New York, NY (2012). doi: 10.1145/2330784.2330964
  68. 68.
    NVIDIA: NVIDIA CUDA Programming Guide Accessed Jul 2013Google Scholar
  69. 69.
    Platoš, J., Snášel, V., Ježowicz, T., Krömer, P., Abraham, A.: A PSO-based document classification algorithm accelerated by the CUDA platform. In: Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on, pp. 1936–1941 (2012). doi: 10.1109/ICSMC.2012.6378021
  70. 70.
    Pop, F.: Optimization of resource control for transitions in complex systems. Math. Probl. Eng. 12 (2012). doi: 10.1155/2012/625861
  71. 71.
    Pospíchal, P., Jaroš, J. Schwarz, J.: Parallel genetic algorithm on the CUDA architecture. In: Di Chio, C. et al. (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, pp. 442–451. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-12239-2_46
  72. 72.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution a Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)
  73. 73.
    Pryor, G., Lucey, B., Maddipatla, S., McClanahan, C., Melonakos, J., Venugopalakrishnan, V., Patel, K., Yalamanchili, P., Malcolm, J.: High-level GPU computing with Jacket for Matlab and C/C++. In: Modeling and Simulation for Defense Systems and Applications VI, vol. 8060, pp. 806,005–806,005–6 (2011). doi: 10.1117/12.884899
  74. 74.
    Qin, A.K., Raimondo, F., Forbes, F., Ong, Y.S.: An improved CUDA-based implementation of differential evolution on GPU. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, GECCO ’12, pp. 991–998. ACM, New York, NY (2012). doi: 10.1145/2330163.2330301
  75. 75.
    Rabinovich, M., Kainga, P., Johnson, D., Shafer, B., Lee, J., Eberhart, R.: Particle Swarm Optimization on a GPU. In: Electro/Information Technology (EIT), 2012 IEEE International Conference on, pp. 1–6 (2012). doi: 10.1109/EIT.2012.6220761
  76. 76.
    Ramirez-Chavez, L.E., Coello Coello, C.A., Rodriguez-Tello, E.: A GPU-based implementation of differential evolution for solving the gene regulatory network model inference problem. In: Proceedings of the Fourth International Workshop on Parallel Architectures and Bioinspired Algorithms, WPABA 2011, pp. 21–30. Galveston Island, TX, USA (2011)Google Scholar
  77. 77.
    Reguera-Salgado, J., Martin-Herrero, J.: High performance GCP-based Particle Swarm Optimization of orthorectification of airborne pushbroom imagery. In: Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE, International, pp. 4086–4089 (2012). doi: 10.1109/IGARSS.2012.6350729
  78. 78.
    Roberge, V., Tarbouchi, M.: Efficient parallel particle swarm optimizers on GPU for real-time harmonic minimization in multilevel inverters. In: IECON 2012—38th Annual Conference on IEEE Industrial Electronics Society, pp. 2275–2282 (2012). doi: 10.1109/IECON.2012.6388882
  79. 79.
    Roberge, V., Tarbouchi, M.: Parallel particle swarm optimization on graphical processing unit for pose estimation. WSEAS Trans. Comput. 11, 170–179 (2012)Google Scholar
  80. 80.
    Robilliard, D., Marion-Poty, V., Fonlupt, C.: Genetic programming on graphics processing units. Genet. Program Evolvable Mach., 10, 447–471, Kluwer Academic Publishers (2009)Google Scholar
  81. 81.
    Rutenbar, R.: Simulated annealing algorithms: an overview. Circuits Devices Mag. IEEE 5(1), 19–26 (1989). doi: 10.1109/101.17235 CrossRefGoogle Scholar
  82. 82.
    Schröck, M., Vogt, H.: Gauge fixing using overrelaxation and simulated annealing on GPUs. PoS LATTICE2012, 187 (2012)Google Scholar
  83. 83.
    Sharma, B., Thulasiram, R., Thulasiraman, P.: Portfolio management using particle swarm optimization on GPU. In: Parallel and Distributed Processing with Applications (ISPA), 2012 IEEE 10th International Symposium on, pp. 103–110 (2012). doi: 10.1109/ISPA.2012.22
  84. 84.
    Sharma, B., Thulasiram, R., Thulasiraman, P.: Normalized particle swarm optimization for complex chooser option pricing on graphics processing unit. J. Supercomput., 1–23 (2013). doi: 10.1007/s11227-013-0893-z
  85. 85.
    Simonsen, M., Pedersen, C.N., Christensen, M.H., Thomsen, R.: GPU-accelerated high-accuracy molecular docking using guided differential evolution: real world applications. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO’11, pp. 1803–1810. ACM, New York, NY (2011). doi: 10.1145/2001576.2001818
  86. 86.
    Souza, D.L., Teixeira, O.N., Monteiro, D.C., Oliveira, R.C.L.A.: A new cooperative evolutionary multi-swarm optimizer algorithm based on CUDA architecture applied to engineering optimization. In: Hatzilygeroudis, I., Palade, V. (ed.) Combinations of Intelligent Methods and Applications, Smart Innovation, Systems and Technologies, vol. 23, pp. 95–115. Springer, Berlin (2013). doi: 10.1007/978-3-642-36651-2_6
  87. 87.
    Stivala, A., Stuckey, P., Wirth, A.: Fast and accurate protein substructure searching with simulated annealing and GPUs. BMC Bioinform. 11(1), 1–17 (2010). doi: 10.1186/1471-2105-11-446 CrossRefGoogle Scholar
  88. 88.
    Storn, R.: Differential evolution design of an IIR-filter. In: Proceeding of the IEEE Conference on Evolutionary Computation ICEC, pp. 268–273. IEEE Press (1996)Google Scholar
  89. 89.
    Storn, R., Price, K.: Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report (1995).
  90. 90.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real Parameter Optimization. Technical Report. Nanyang Technological University (2005)Google Scholar
  91. 91.
    Tagawa, K.: Concurrent differential evolution based on generational model for multi-core CPUs. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL, Lecture Notes in Computer Science, vol. 7673, pp. 12–21. Springer (2012)Google Scholar
  92. 92.
    Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark Functions for the CEC 2008 Special Session and Competition on Large Scale Global Pptimization. Technical Report, Nature Inspired Computation and Applications Laboratory, USTC (2007).
  93. 93.
    Tanese, R.: Distributed genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 434–439. Morgan Kaufmann Publishers, Burlington, MA (1989)Google Scholar
  94. 94.
    Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol. 2, pp. 2023–2029. IEEE (2004)Google Scholar
  95. 95.
    Tufts, P.: Parallel case evaluation for Genetic Programming. In: 1993 Lectures in Complex Systems, volume VI of Santa Fe Institute Studies in the Science of Complexity, pp. 591–596. Addison-Wesley, Reading, MA (1995)Google Scholar
  96. 96.
    Ugolotti, R., Nashed, Y., Cagnoni, S.: Real-Time GPU Based Road Sign Detection and Classification. In: Coello, C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) Parallel problem solving from nature—PPSN XII. In: Lecture Notes in Computer Science, vol. 7491, pp. 153–162. Springer, Berlin (2012). doi: 10.1007/978-3-642-32937-1_16
  97. 97.
    Ugolotti, R., Nashed, Y.S., Mesejo, P., Špela Ivekovič, Mussi, L., Cagnoni, S.: Particle swarm optimization and differential evolution for model-based object detection. Appl. Soft Comput. (0), (2012). doi: 10.1016/j.asoc.2012.11.027,
  98. 98.
    Valdez, F., Melin, P., Castillo, O.: Bio-inspired optimization methods on graphic processing unit for minimization of complex mathematical functions. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems, Studies in Computational Intelligence, vol. 451, pp. 313–322. Springer, Berlin (2013). doi: 10.1007/978-3-642-33021-6_25.
  99. 99.
    Wachowiak, M.P., Foster, A.E.L.: GPU-based asynchronous global optimization with particle swarm. J. Phys. Conf. Ser. 385(1), 012,012 (2012). Google Scholar
  100. 100.
    Wang, H., Rahnamayan, S., Wu, Z.: Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. J. Parallel Distrib. Comput. 73(1), 62–73 (2013). doi: 10.1016/j.jpdc.2012.02.019. Metaheuristics on GPUs
  101. 101.
    Wang, L., Yang, B., Chen, Y., Zhao, X.: Predict the hydration of portland cement using differential evolution. In: Evolutionary Computation (CEC), 2012 IEEE Congress on, pp. 1–5 (2012). doi: 10.1109/CEC.2012.6252984
  102. 102.
    Wilt, N.: The CUDA Handbook: A Comprehensive Guide to GPU Programming. Addison-Wesley, Reading, MA (2013)Google Scholar
  103. 103.
    Wong, M., Wong, T.: Implementation of parallel genetic algorithms on graphics processing units. In: Intelligent and Evolutionary Systems, pp. 197–216. Springer, Berlin (2009)Google Scholar
  104. 104.
    Wong, T.T., Wong, M.L.: Parallel evolutionary algorithms on consumer-level graphics processing unit. In: Parallel Evolutionary Computations, pp. 133–155 (2006)Google Scholar
  105. 105.
    Wu, A.S., Lindsay, R.K., Riolo, R.: Empirical observations on the roles of crossover and mutation. In: Bäck, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 362–369. Morgan Kaufmann, San Francisco, CA (1997). Scholar
  106. 106.
    Xiao, C., Qiming, W.: Modified parallel differential evolution algorithm with local spectral feature to solve data registration problems. In: Computer Science and Network Technology (ICCSNT), 2011 International Conference on, vol. 3, pp. 1386–1389 (2011). doi: 10.1109/ICCSNT.2011.6182223
  107. 107.
    Zhang, Z., Seah, H.S.: CUDA acceleration of 3D dynamic scene reconstruction and 3D motion estimation for motion capture. In: Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on, pp. 284–291 (2012). doi: 10.1109/ICPADS.2012.47
  108. 108.
    Zhang, S., He, Z.: Implementation of parallel genetic algorithm based on CUDA. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 24–30. Springer, Heidelberg (2009)Google Scholar
  109. 109.
    Zhao, J., Wang, W., Pedrycz, W., Tian, X.: Online parameter optimization-based prediction for converter gas system by parallel strategies. Control Syst. Technol. IEEE Trans. 20(3), 835–845 (2012). doi: 10.1109/TCST.2011.2134098 CrossRefGoogle Scholar
  110. 110.
    Zhu, W.: Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems. J. Glob. Optim., 1–21 (2010). doi: 10.1007/s10898-010-9590-0
  111. 111.
    Zhu, W., Li, Y.: GPU-accelerated differential evolutionary markov chain Monte Carlo method for multi-objective optimization over continuous space. In: Proceeding of the 2nd Workshop on Bio-Inspired Algorithms for Distributed Systems, BADS ’10, pp. 1–8. ACM, New York, NY (2010). doi: 10.1145/1809018.1809021

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.IT4Innovations and Department of Computer ScienceVŠB-Technical University of OstravaPoruba, OstravaCzech Republic

Personalised recommendations