A Parallel Adaptive Swarm Search Framework for Solving Black-Box Optimization Problems

  • Romeo ShukaEmail author
  • Jürgen Brehm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11479)


This paper presents a framework to support parallel swarm search algorithms for solving black-box optimization problems. Looking at swarm based optimization, it is important to find a well fitted set of parameters to increase the convergence rate for finding the optimum. This fitting is problem dependent and time-consuming. The presented framework automates this fitting. After finding parameters for the best algorithm, a good mapping of algorithmic properties onto a parallel hardware is crucial for the overall efficiency of a parallel implementation. Swarm based algorithms are population based, the best number of individuals per swarm and, in the parallel case, the best number of swarms in terms of efficiency and/or performance has to be found. Data dependencies result in communication patterns that have to be cheaper in terms of execution times than the computing in between communications. Taking all this into account, the presented framework enables the programmer to implement efficient and adaptive parallel swarm search algorithms. The approach is evaluated through benchmarks and real world problems.


Particle Swarm Optimization Parallelization Adaptive algorithm Optimization problems Interplanetary space trajectory 


  1. 1.
    Abadlia, H., Smairi, N., Ghedira, K.: Particle swarm optimization based on dynamic island model. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 709–716 (2017)Google Scholar
  2. 2.
    Addis, B., et al.: A global optimization method for the design of space trajectories. Comput. Optim. Appl. 48(3), 635–652 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    European Space Agency and Advanced Concepts Team: Global Trajectory Optimization Problems Database, 19 November 2018.
  4. 4.
    European Space Agency and Advanced Concepts Team: Messenger (Full Version), 19 November 2018.
  5. 5.
    Ahmed, H.: An Efficient Fitness-Based Stagnation Detection Method for Particle Swarm Optimization (2014)Google Scholar
  6. 6.
    Alam, M., Das, B., Pant, V.: A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination. Electr. Power Syst. Res. 128, 39–52 (2015)CrossRefGoogle Scholar
  7. 7.
    Allugundu, I., et al.: Acceleration of distance-to-default with hardware-software co-design, August 2012, pp. 338–344 (2012)Google Scholar
  8. 8.
    Altinoz, O.T., Yılmaz, A.E.: Comparison of Parallel CUDA and OpenMP Implementations of Particle Swarm OptimizationGoogle Scholar
  9. 9.
    Bonyadi, M.R., Michalewicz, Z.: Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 20, 370–385 (2016). ISSN 1089–778XCrossRefGoogle Scholar
  10. 10.
    Chen, T.-Y., Chi, T.-M.: On the improvements of the particle swarm optimization algorithm. Adv. Eng. Soft. 41(2), 229–239 (2010)CrossRefGoogle Scholar
  11. 11.
    Clerc, M.: Standard Particle Swarm Optimization, 19 November 2018.
  12. 12.
    Molga, M., Smutnicki, C.: Test functions for optimization needs, 19 November 2018.
  13. 13.
    Isikveren, A., et al.: Optimization of commercial aircraft utilizing battery-based voltaic-joule/Brayton propulsion. J. Airc. 54, 246–261 (2016)CrossRefGoogle Scholar
  14. 14.
    Jong-Yul, K., et al.: PC cluster based parallel PSO algorithm for optimal power flow. In: Proceedings of the International Conference on Intelligent Systems Applications to Power Systems (2007)Google Scholar
  15. 15.
    Kahar, N.H.B.A., Zobaa, A.F.: Optimal single tuned damped filter for mitigating harmonics using MIDACO. In: 2017 IEEE International Conference on Environment and Electrical Engineering (2017)Google Scholar
  16. 16.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks (1995)Google Scholar
  18. 18.
    Laguna-Sánchez, G.A., et al.: Comparative study of parallel variants for a particle swarm optimization algorithm implemented on a multithreading GPU. J. Appl. Res. Technol. 7, 292–307 (2009)Google Scholar
  19. 19.
    Latter, B.D.H.: The island model of population differentiations: a general solution. Genetics 73(1), 147–157 (1973)MathSciNetGoogle Scholar
  20. 20.
    Liu, Z., et al.: OpenMP-based multi-core parallel cooperative PSO with ICS using machine learning for global optimization problem. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics (2015)Google Scholar
  21. 21.
    Mahajan, N.R., Mysore, S.P.: Combinatorial neural inhibition for stimulus selection across space. bioRxiv (2018)Google Scholar
  22. 22.
    Roberge, V., Tarbouchi, M.: Comparison of parallel particle swarm optimizers for graphical processing units and multicore processors. J. Comput. Intell. Appl. 12, 1350006 (2013)CrossRefGoogle Scholar
  23. 23.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation (1998)Google Scholar
  24. 24.
    Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). Scholar
  25. 25.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997). ISSN 1573–2916MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)CrossRefGoogle Scholar
  27. 27.
    Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: Proceedings of the Congress on Evolutionary Computation (2013)Google Scholar
  28. 28.
    Zhang, J., et al.: A fast restarting particle swarm optimizer. In: 2014 IEEE Congress on Evolutionary Computation (CEC) (2014)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Systems EngineeringLeibniz Universität HannoverHannoverGermany

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