Here is a very simple version of PSO in Matlab. PSO is a very popular, population based metaheuristic algorithm that mimics swarming behavior and swarm intelligence in order to solve optimization problems.
The code below is intended to get you started working with PSO in Matlab or Octave. Best efforts were made to keep the code clean and easy to understand. Feel free to play with it and contact me with any questions.
Some references for GA on GPU. If you know of any further resources, please contact me.
[1] Q. Yu, C. Chen, and Z. Pan, “Parallel genetic algorithms on programmable graphics hardware,” in Lecture Notes in Computer Science
3612. Springer, 2005, p. 1051.
[2] P. Pospichal and J. Jaros, “GPU-based Acceleratino of the Genetic Algorithm,” in Proceedings of GECCO 2009, 2009.
[3] A. Munawar, M. Wahib, M. Munetomo, and K. Akama, “Hybrid of genetic algorithm and local search to solve max-sat problem using NVIDIA CUDA framework,” Genetic Programming and Evolvable Machines, vol. 10, pp. 391–415, 2009.
[4] S. Debattistic, N. Marlat, L. Mussi, and S. Cagnoni, “Implementatino of a Simple Genetic Algorithm within the CUDA Architecture,” in Proceedings of GECCO 2009, 2009.
[5] S. Zhang and Z. He, “Implementation of parallel genetic algorithm based on cuda,” in Advances in Computation and Intelligence, ser. Lecture Notes in Computer Science, Z. Cai, Z. Li, Z. Kang, and Y. Liu, Eds. Springer Berlin / Heidelberg, 2009, vol. 5821, pp. 24–30.
[6] S. Tsutsui and N. Fujimoto, “Solving quadratic assignment problems by genetic algorithms with gpu computation: a case study,” in Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, ser. GECCO ’09. New York, NY, USA: ACM, 2009, pp. 2523–2530.
[7] P. Vidal and E. Alba, “A multi-gpu implementation of a cellular genetic algorithm,” in 2010 IEEE Congress on Evolutionary Computation (CEC), July 2010, pp. 1–7.
[8] ——, “Cellular genetic algorithm on graphic processing units,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), ser. Studies in Computational Intelligence, J. Gonzlez, D. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds. Springer Berlin/Heidelberg, 2010, vol. 284, pp. 223–232.
[9] R. Arora, R. Tulshyan, and K. Deb, “Parallelization of binary and real-coded genetic algorithms on GPU using CUDA,” in IEEE Congress on Evolutionary Computation, 2010, pp. 1–8.
[10] N. Fujimoto and S. Tsutsui, “A highly-parallel tsp solver for a gpu computing platform,” in Proceedings of the 7th international conference on Numerical methods and applications, ser. NMA’10. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 264–271.
Some references for AIS on GPU. If you know of any further resources, please contact me.
[1] J. Zhao, Q. Liu, W. Wang, Z. Wei, and P. Shi, “A parallel immune algorithm for traveling salesman problem and its application on cold
rolling scheduling,” Information Sciences, vol. 181, no. 7, pp. 1212 – 1223, 2011.
[2] J. Li, L. Zhang, and L. Liu, “A Parallel Immune Algorithm Based on Fine-grained Model with GPU-Acceleration,” in Foruth International
Conference on Innovative Computing, Information, and Control, 2009, pp. 683–686.
Some references for ACO on GPU. If you know of any further resources, please contact me.
[1] J. Li, X. Hu, Z. Pang, and K. Qian, “A Parallel Ant Colony Optimization Algorithm based on Fine-Grained Model with GPU-Acceleration,” Internation Journal of Innovative Computing, Information, and Control, vol. 5, no. 11(A), November 2009.
[2] Y.-S. You, “Parallel ant system for traveling salesman problem on GPUs,” in Proceedings of GECCO 2009, 2009.
[3] S. Sanci, “A Parallel Algorithm for Flight Route Planning on GPU using CUDA,” Master’s thesis, Middle East Technical University,
Turkey, 2010.
[4] J. M. Cecilia, J. M. Garca, M. Ujaldon, A. Nisbet, and M. Amos, “Parallelization strategies for ant colony optimisation on GPUs,”
Computing Research Repository, pp. –1–1, 2011.