No Access

A Novel Approach for Optimization in Dynamic Environments Based on Modified Artificial Fish Swarm Algorithm

Danial Yazdani

Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

E-mail Address: d_yazdani@mshdiau.ac.ir

Search for more papers by this author

Alireza Sepas-Moghaddam

Department of Electrical and Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa Lisbon, Portugal

E-mail Address: alireza.sepas-moghaddam@tecnico.ulisboa.pt

E-mail Address: sepasmoghaddam@gmail.com

Corresponding author.

Search for more papers by this author

Atabak Dehban

Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa Lisbon, Portugal

E-mail Address: adehban@isr.ist.utl.pt

Search for more papers by this author

, and

Nuno Horta

Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa Lisbon, Portugal

E-mail Address: nuno.horta@lx.it.pt

Search for more papers by this author

https://doi.org/10.1142/S1469026816500103Cited by:24 (Source: Crossref)

Abstract

Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

1. T. Nguyen, Continuous dynamic optimization using evolutionary algorithms, Ph.D. Thesis, The University of Birmingham, UK (2010). Google Scholar
2. T. Blackwell and J. Branke, Multiswarms, exclusion, and anti-convergence in dynamic environments, IEEE Trans. Evolut. Comput. 10 (2006) 459–472. Crossref, Google Scholar
3. S. Yang and C. Li, A Clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments, IEEE Trans. Evolut. Comput. 14 (2010) 959–974. Crossref, Google Scholar
4. C. Li and S. Yang, A general framework of multi-population methods with clustering in undetectable dynamic environments, IEEE Trans. Evolut. Comput. 16 (2012) 556–577. Crossref, Google Scholar
5. Y. Jin and J. Branke, Evolutionary optimization in uncertain environments—A survey, IEEE Trans. Evolut. Comput. 9 (2005) 303–317. Crossref, Google Scholar
6. J. Kennedy and R. Eberhart, Particle swarm optimization, proc. IEEE Int. Conf. Neural Networks, 27 November–1 December 1995, Perth, WA, pp. 1942–1948. Google Scholar
7. D. Angus and T. Hendtlass, Dynamic ant colony optimization, Appl. Intell. 23 (2005) 23–38. Crossref, Google Scholar
8. M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization, IEEE Comput. Intell. Mag. 1 (2006) 28–39. Google Scholar
9. M. C. Plessis and A. P. Engelbrecht, Differential evolution for dynamic environments with unknown numbers of optima, J. Glob. Optim. 55 (2013) 73–99. Crossref, Google Scholar
10. V. Noroozi et al., CellularDE: A cellular based differential evolution for dynamic optimization problems, Adaptive and Natural Computing Algorithms (Springer, Berlin Heidelbung, Germany, 2011), pp. 340–349. Crossref, Google Scholar
11. D. Yazdani, M. R. Akbarzadeh-tonchi, B. Nasiri and M. R. Meybodi, A new artificial fish swarm algorithm for dynamic optimization problems, Proc. IEEE Congress on Evolutionary Computation, 10–15 June 2012, Brisbane, QLD, pp. 1–8. Google Scholar
12. J. Branke, Memory enhanced evolutionary algorithms for changing optimization problems, Evolut. Comput. 3 (1999) 1875–1882. Google Scholar
13. J. Branke, Moving peaks benchmark (2016) http://people.aifb.kit.edu/jbr/MovPeaks/. Google Scholar
14. A. Uyar and A. E. Harmanci, A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments, Soft Comput. 9 (2005) 803–814. Crossref, Google Scholar
15. S. Yang, Memory-based immigrants for genetic algorithms in dynamic environments, Proc. 7th Ann. Conf. Genetic and Evolutionary Computation, 25–29 June 2005, New York, pp. 1115–1122. Google Scholar
16. H. Richter, Memory design for constrained dynamic optimization problems, Applications of Evolutionary Computation, Lecture Notes in Computer Science Vol. 6024 (Springer, Germany, 2010), pp. 552–561. Crossref, Google Scholar
17. A. Simões and E. Costa, Evolutionary algorithms for dynamic environments: Prediction using linear regression and Markov chains, Parallel Problem Solving from Nature—PPSNX, Lecture Notes in Computer Science Vol. 5199 (Springer, Germany, 2008), pp. 306–315. Crossref, Google Scholar
18. H. Richter and S. Yang, Memory based on abstraction for dynamic fitness functions, Applications of Evolutionary Computing, Lecture Notes in Computer Science Vol. 4974 (Springer, Germany, 2008), pp. 596–605. Crossref, Google Scholar
19. S. Yang and Y. Xin, Population-based incremental learning with associative memory for dynamic environments, IEEE Trans. Evolut. Comput. 12 (2008) 542–561. Crossref, Google Scholar
20. R. W. Morrison, Designing Evolutionary Algorithms for Dynamic Environments (Springer-Verlag, New York, 2004). Crossref, Google Scholar
21. J. Grefenstette, Genetic algorithms for changing environments, Proc. 5th Int. Conf. Genetic Algorithms, Amsterdam, 1992 (Morgan Kaufmann, San Francisco, CA, 1992) pp. 137–144. Google Scholar
22. L. T. Bui, H. A. Abbass and J. Branke, Multiobjective optimization for dynamic environments, Proc. IEEE Congress on in Evolutionary Computation, Edinburgh, 2005 Vol. 3 (IEEE, Piscataway, NJ, 2005) pp. 2349–2356. Google Scholar
23. H. Andersen, An investigation into genetic algorithms, and the relationship between speciation and the tracking of optima in dynamic functions, Ph.D. Thesis, Queensland University of Technology, Brisbane, Australia (1991). Google Scholar
24. F. Oppacher and M. Wineberg, The shifting balance genetic algorithm: Improving the GA in a dynamic environment, in Proc. Genetic and Evolutionary Computation Conf. (GECCO 1999), Vol. 1 (Morgan kaufmann, San Francisco, CA, 1999), 250 (Springer-Verlag, Germany, 2008), pp. 504–510. Google Scholar
25. T. Blackwell, J. Branke and X. Li, Particle swarms for dynamic optimization problems, Swarm Intelligence 193–217. Google Scholar
26. H. Cheng and S. Yang, Multi-population Genetic Algorithms with Immigrants Scheme for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks, Applications of Evolutionary Computation, Lecture Notes in Computer Science Vol. 6024 (Springer, Germany, 2011), pp. 562–571. Crossref, Google Scholar
27. H. C. Tsai and Y. H. Lin, Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior, Appl. Soft Comput. 11 (2011) 5367–5374. Crossref, Google Scholar
28. M. Kamosi et al., A new particle swarm optimization algorithm for dynamic environments, Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science Vol. 6466 (Springer, Germany, 2010), pp. 129–138. Crossref, Google Scholar
29. L. Changhe and Y. Shengxiang, Fast multinswarm optimization for dynamic optimization problems, Proc 4th Int. Conf. Natural Computation, Shandong, 2008, (IEEE Computer Society, LA, 2008) pp. 624–628. Google Scholar
30. R. K. Ursem et al., Multinational GAs: Multimodal optimization techniques in dynamic environments, in Proc the 2nd Genetic and Evolutionary Computation Conf., 2000, (Morgan Kaufmann, San Francisco, 2000) pp. 19–26. Google Scholar
31. X. Li, Z. Shao and J. Qian, An optimization method base on autonomous animates: fish swarm algorithm, Syst. Eng. Theory Pract. 22 (2002) 32–38. Google Scholar
32. W. Shen, X. Guo, C. Wu and D. Wu, Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm, Knowl.-Based Syst. 24 (2011) 378–385. Crossref, Google Scholar
33. D. Yazdani, H. Nabizadeh, E. M. Kosari and A. N. Tossi, Color quantization using modified artificial fish swarm algorithm, A1 2011: Advances in Artificial Intelligence (Springer-Verlag, Germany, 2011), pp. 382–391. Crossref, Google Scholar
34. A. M. A. C. Rocha, T. F. M. C. Martins and E. M. G. P. Fernandes, An augmented Lagrangian fish swarm based method for global optimization, J. Comput. Appl. Math. 235 (2011) 4611–4620. Crossref, Google Scholar
35. D. Yazdani, A. N. Tossi and M. R. Meybodi, Fuzzy adaptive artificial fish swarm algorithm, A1 2010: Advances in Artificial Intelligence (Springer-Verlag, Germany, 2010), pp. 334–343. Crossref, Google Scholar
36. W. Zhu, J. Jiang, C. Song and L. Bao, Clustering algorithm based on fuzzy C-means and artificial fish swarm, Procedia Eng. 29 (2012) 3307–3311. Crossref, Google Scholar
37. Y. Cheng, M. Jiang and D. Yuan, Novel clustering algorithms based on improved artificial fish swarm algorithm, Proc. 6th Int. Conf. Fuzzy Systems and Knowledge Discovery, 14–16 August 2009, Tianjin, pp. 141–145. Google Scholar
38. D. Yazdani, B. Saman, A. Sepas-Moghadam, F. Mohammad-Kazemi and M. R. Meybodi, A new algorithm based on improved artificial fish swarm algorithm for data clustering, Int. J. Artif. Intell. 11 (2013) 193–221. Google Scholar
39. M. Jiang and K. Zhu, Multiobjective optimization by artificial fish swarm algorithm, proc IEEE Int. conf. Computer Science and Automation Engineering, 10–12 June 2011, Shanghai, pp. 506–511. Google Scholar
40. A. Cheng and X. Hong, PID controller parameters optimization based on artificial fish swarm algorithm, Proc. 5th Int. Conf. Intelligent Computation Technology and Automation, 12–14 January 2012, Zhangjiajie, Hunan, pp. 265–268. Google Scholar
41. D. Yazdani, A. Arabshahi, A. Sepas-Moghaddam and M. Dehshibi, A multilevel thresholding method for image segmentation using a novel hybrid intelligent approach, Proc. 12th Int. Conf. Hybrid Intelligent Systems, December 2012, Puna, pp. 137–142. Google Scholar
42. A. Sepas-Moghaddam, D. Yazdani and J. Shahabi, A novel hybrid image segmentation method, Prog. Artif. Intell. 3 (2014) 39–41. Crossref, Google Scholar
43. D. Yazdani, B. Nasiri, A. Sepas-Moghaddam, M. Meybodi and M. Akbarzadeh-Totonchi, mNAFSA: A novel approach for optimization in dynamic environments with global changes, Swarm Evolut. Comput. 18 (2014) 38–53. Crossref, Google Scholar
44. M. Kamosi, A. Hashemi and M. Meybodi, A hibernating multi-swarm optimization algorithm for dynamic environments, Proc. World Congress on Nature and Biologically Inspired Computing, 19–21 October 2010, Kitakyushu, pp. 370–376. Google Scholar
45. I. Rezazadeh, M. Meybodi and A. Naebi, Adaptive particle swarm optimization algorithm for dynamic environments, Advances in Swarm Intelligence, Lecture Notes in Computer Science Vol. 6728 (Springer, Germany, 2011), pp. 120–129. Crossref, Google Scholar
46. A. Hashemi and M. Meybodi, Cellular Pso: A Pso for Dynamic Environments, Advances in Computation and Intelligence, Lecture Notes in Computer Science Vol. 5821 (Springer-Verlag, Germany, 2009), pp. 422–433. Crossref, Google Scholar
47. L. Liu et al., Particle swarm optimization with composite particles in dynamic environments, IEEE Trans. Sys. Man Cybern. B, Cybern. 40 (2010) 1634–1648. Crossref, Google Scholar
48. S. Bird and X. Li, Using regression to improve local convergence, Proc. IEEE Congress on Evolutionary Computation, 25–28, September 2007, Singapore, pp. 592–599. Google Scholar
49. W. Du and B. Li, Multi-strategy ensemble particle swarm optimization for dynamic optimization, Inf. Sci. 178 (2008) 3096–3109. Crossref, Google Scholar
50. Y. G. Woldesenbet and G. G. Yen, Dynamic evolutionary algorithm with variable relocation, IEEE Trans. Evolut. Comput. 13 (2009) 500–513. Crossref, Google Scholar
51. B. Nasiri and M. Meybodi, Speciation based firefly algorithm for optimization in dynamic environments, Int. J. Artif. Intell. 8 (2012) 118–132. Google Scholar
52. J. K. Kordestani, A. Rezvanian and M. R. Meybodi, CDEPSO: a bi-population hybrid approach for dynamic optimization problems, Appl. Intell. 40 (2014) 682–694. Crossref, Google Scholar
53. S. Nabizadeh, A. Rezvanian and M. R. Meybodi, Tracking extrema in dynamic environments using multi-swarm cellular pso with local search, Int. J. Electron. Inf. 1 (2012) 29–37. Google Scholar
54. D. Yazdani, B. Nasiri, R. Azizi, A. Sepas-Moghadam and M. R. Meybodi, Optimization in dynamic environment utilizing a novel method based on particle swarm optimization, Int. J. Artif. Intell. 11 (2013) 170–192. Google Scholar
55. B. Nasiri and M. R. Meybodi, Improved speciation-based Firefly Algorithm in dynamic and uncertain environment, Int. J. Bio-Inspir. Comput. (2016) In press. Crossref, Google Scholar