1887

Abstract

Summary

We compare the performance of six recent global optimization algorithms: Imperialist Competitive Algorithm (ICA), Firefly Algorithm (FA), Water Cycle Algorithm (WCA), Whale Swarm Optimization (WSO), Fireworks Algorithm (FWA) and Quantum Particle Swarm Optimization (QPSO). These methods have been introduced in the last few years and have found very limited or no applications in geophysical exploration problems thus far. The methods are first tested on two multi-minima analytic objective functions often used to test optimization algorithms: The Rastrigin and the Schwefel functions. Then, they are compared on the residual statics corrections, which is a highly non-linear geophysical optimization problem. In particular, we are interested in testing the convergence capabilities of these methods as the number of unknown model parameters increases. The different approaches are compared against a standard implementation of the Particle Swarm optimization (PSO), that is a popular global search method. The tests on the analytical functions and on the residual statics corrections demonstrate that FA, WCA, WSO and FWA outperform the other approaches in solving multi-minima and high-dimensional optimization problems. Conversely, PSO and ICA show limited exploration capabilities and lower convergence rates with respect to the other approaches.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901383
2019-06-03
2024-04-24
Loading full text...

Full text loading...

References

  1. Atashpaz-Gargari, E., and Lucas, C.
    (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary computation Congress2007, 4661–4667.
    [Google Scholar]
  2. Eberhart, R., and Kennedy, J.
    (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, 39–43.
    [Google Scholar]
  3. Eskandar, H., Sadollah, A., Bahreininejad, A., and Hamdi, M.
    (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151–166.
    [Google Scholar]
  4. Mirjalili, S., and Lewis, A.
    (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
    [Google Scholar]
  5. Tan, Y., and Zhu, Y.
    (2010). Fireworks algorithm for optimization. Advances in Swarm Intelligence. Springer, Berlin, Heidelberg, 355–364.
    [Google Scholar]
  6. Sajeva, A., Aleardi, M., Galuzzi, B., Stucchi, E., Spadavecchia, E., and Mazzotti, A.
    (2017). Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full-waveform inversion, and residual static computation. Geophysical Prospecting, 65, 322–346.
    [Google Scholar]
  7. Sen, M. K., and Stoffa, P. L.
    (2013). Global optimization methods in geophysical inversion. Cambridge University Press.
  8. Sun, J., Feng, B., and Xu, W.
    (2004). Particle swarm optimization with particles having quantum behavior. In Evolutionary Computation, 2004, 1, 325–331.
    [Google Scholar]
  9. Yang, X. S.
    (2008). Firefly algorithm. Nature-inspired metaheuristic algorithms, Luniver Press.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901383
Loading
/content/papers/10.3997/2214-4609.201901383
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error