Skip to main content

Advertisement

SpringerLink
Log in

A computational ecosystem for optimization: review and perspectives for future research

  • Regular research paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Nature exhibits extremely diverse, dynamic, robust, complex and fascinating phenomena and, since long ago, it has been a great source of inspiration for solving hard and complex problems in computer science. Hence, the search for plausible biologically inspired ideas, models and computational paradigms always drew the interest of computer scientists. It is worth mentioning that most bio-inspired algorithms only focuses on and took inspiration from specific aspects of the natural phenomena. However, in nature, biological systems are interlinked to each other, e.g., biological ecosystems. The ecosystem as a whole can be composed by species that respond to environmental and ecological stimuli. This work reviews the theoretical foundations and applications of a computational ecosystem for optimization, named ECO. Also, as some concepts and processes inherent to biological ecosystems have already been explored in the ECO approach, some related works are described. Finally, several future research directions are pointed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Web site: https://computing.llnl.gov/tutorials/pthreads/.

References

  1. Antunes R, Leymarie F (2013) Real-time behavioral animation of humanoid non-player characters with a computational ecosystem. In: Aylett R, Krenn B, Pelachaud C, Shimodaira H (eds) Intelligent virtual agents, Lecture notes in Computer Science, vol 8108. Springer, Berlin, pp 382–395

  2. Begon M, Townsend CR, Harper JL (2006) Ecology: from individuals to ecosystems, 4th edn. Blackwell Publishing, Oxford

    Google Scholar 

  3. Benítez CMV, Parpinelli RS, Lopes HS (2012) Parallelism, hybridism and coevolution in a multi-level ABC-GA approach for the protein structure prediction problem. Concurr Comput Pract Exp 24(6):635–646

    Article  Google Scholar 

  4. Benítez CMV, Parpinelli RS, Lopes HS (2013) A heterogeneous parallel ecologically-inspired approach applied to the 3D-AB off-lattice protein structure prediction problem. In: Proceedings of the 1st BRICS countries congress (BRICS-CCI) and 11th brazilian congress (CBIC) on computational intelligence

  5. Binitha S, Sathya SS (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng 2(2):137–151

    Google Scholar 

  6. Briscoe G, de Wilde P (2008) Digital ecosystems: optimisation by a distributed intelligence. Proceedings of IEEE international conference on digital ecosystems and technologies. IEEE Press, Phitsanulok, pp 192–197

    Google Scholar 

  7. Briscoe G, de Wilde P (2009) Computing of applied digital ecosystems. Proceedings of the international conference on management of emergent digital ecosystems. ACM Press, Lyon, pp 28–35

    Chapter  Google Scholar 

  8. Clerc M (2006) Particle swarm optimization. ISTE Press, London

    Book  MATH  Google Scholar 

  9. Dalgaard P (2008) Introductory statistics with R, 2nd edn. Springer, NY

    Book  MATH  Google Scholar 

  10. Dasgupta D, Yu S, Nio F (2011) Review article: recent advances in artificial immune systems: models and applications. Appl Soft Comput 11(2):1574–1587

    Article  Google Scholar 

  11. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  12. El-Abd M, Kamel M (2005) A taxonomy of cooperative search algorithms. Proceedings of the second international conference on hybrid metaheuristics, Lecture notes in computer science, vol 3636. Springer, Berlin, pp 32–41

  13. Engelbrecht AP (2007) Computational intelligence: an introduction, 2nd edn. John Wiley & Sons, Chichester

    Book  Google Scholar 

  14. Floudas CA, Pardalos PM (1990) A collection of test problems for constrained global optimization problems, vol 455. Lecture notes in computer ScienceSpringer, Berlin

    Book  MATH  Google Scholar 

  15. Ganguly N, Sikdar BK, Deutsch A, Canright G, Chaudhuri PP (2003) A survey on cellular automata. Center of High-Performance Computing, Dresden University of Technology, Dresden, Tech. rep.

    Google Scholar 

  16. Glance N, Hogg T, Huberman BA (1991) Computational ecosystems in a changing environment. Int J Mod Phys C 2(3):735–753

  17. Gong W, Cai Z, Ling CX (2010) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15(4):645–665

    Article  Google Scholar 

  18. González J, Masegosa A, García I (2011) A cooperative strategy for solving dynamic optimization problems. Memet Comput 3(1):3–14

    Article  Google Scholar 

  19. Kaplan D, Glass L (1995) Understanding nonlinear dynamics. Springer, New York

    Book  MATH  Google Scholar 

  20. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    Article  MATH  MathSciNet  Google Scholar 

  21. Kari J (2005) Theory of cellular automata: a survey. Theor Comput Sci 334(1):3–33

    Article  MATH  MathSciNet  Google Scholar 

  22. Kennedy J, Eberhart R (2001) Swarm intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  23. Kephart JO, Hogg T, Huberman BA (1989) Dynamics of computational ecosystems. Phys Rev A 40:404–421

    Article  MathSciNet  Google Scholar 

  24. Krause J, Cordeiro J, Parpinelli RS, Lopes HS (2013) A survey on swarm algorithms applied to discrete problems. In: Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (eds) Swarm intelligence and bio-Inspired computation: theory and applications. Elsevier Science, Amsterdam, pp 169–192

    Chapter  Google Scholar 

  25. Legendre P, Legendre L (1998) Numerical ecology. Elsevier, Amsterdam

    MATH  Google Scholar 

  26. Lopes H (2008) Evolutionary algorithms for the protein folding problem: a review and current trends. In: Smolinski T, Milanova M, Hassanien AE (eds) Computational intelligence in biomedicine and bioinformatics, vol 1. Springer, Berlin, pp 297–315

    Chapter  Google Scholar 

  27. Lung R, Chira C, Dumitrescu D (2012) Guest editorial: special issue on nature inspired cooperative strategies for optimization. Memet Comput 4(4):247–248

    Article  Google Scholar 

  28. Ma ZS (2011) Ecological theatre for evolutionary computing play: some insights from population ecology and evolutionary ecology. Int J Bio-Inspired Comput 3(1):31–55

    Article  Google Scholar 

  29. Malik N (2005) Artificial neural networks and their applications. In: Conference on unearthing technological developments and their transfer to serving masses. ITM, Mathura

  30. Masegosa AD, Pelta DA, García del Amo I, Verdegay JL (2008) On the performance of homogeneous and heterogeneous cooperative search strategies. In: Krasnogor N, Melin-Batista B, Moreno-Prez JA, Moreno-Vega JM, Pelta DA (eds) International workshop on nature inspired cooperative strategies for optimization, studies in computational intelligence. Springer, Berlin, pp 287–300

    Google Scholar 

  31. May RMC, McLean AR (2007) Theoretical ecology: principles and applications. Oxford University Press, Oxford

    Google Scholar 

  32. Meirelles M, Jr., G, Fausto A, Araújo R (2010) Diversity-based adaptive evolutionary algorithms. Korosec P (ed) New achievements in evolutionary computation. InTech, Rijeka, pp 1–16

  33. Murtagh F, Contreras P (2012) Algorithms for hierarchical clustering: an overview. Data Min Knowl Discov 2(1):86–97

    Article  MathSciNet  Google Scholar 

  34. Olofsson P (2005) Probability, statistics, and stochastic processes. Wiley-Interscience, New York

    Book  MATH  Google Scholar 

  35. Parpinelli RS, Benítez CMV, Cordeiro J, Lopes HS (2014) Performance analysis of swarm intelligence algorithms for the 3D-AB off-lattice protein folding problem. J Mult-Valued Logi Soft Comput 22:267–286

  36. Parpinelli RS, Benítez CMV, Lopes HS (2011) Parallel approaches for the artificial bee colony algorithm. In: Panigrani BK, Shi Y, Lim M (eds) Handbook of swarm intelligence: concepts, principles and ppplications. Series: adaptation, learning, and optimization. Springer, Berlin, pp 329–346

  37. Parpinelli RS, Lopes HS (2011) An eco-inspired evolutionary algorithm applied to numerical optimization. Proceedings of the third world congress on nature and biologically inspired computing. IEEE Press, Piscataway, pp 473–478

    Google Scholar 

  38. Parpinelli RS, Lopes HS (2011) New inspirations in swarm intelligence: a survey. Int J Bio-Inspired Comput 3(1):1–16

    Article  Google Scholar 

  39. Parpinelli RS, Lopes HS (2012) Biological plausibility in optimization: an ecosystemic view. Int J Bio-Inspired Comput 4:345–358

    Article  Google Scholar 

  40. Parpinelli RS, Lopes HS (2012) An ecology-based heterogeneous approach for cooperative search. In: Barros L, Finger M, Pozo A (eds) Proceedings of the Brazilian Symposium on Artificial Intelligence (SBIA), Lecture notes in computer Science, vol 7589. Springer, Berlin, pp 212–221

  41. Parpinelli RS, Lopes HS (2012) A hierarchical clustering strategy to improve the biological plausibility of an ecology-based evolutionary algorithm. In: PAVN J. et al. (ed) Ibero-American Conference on Artificial Intelligence (IBERAMIA), Lecture notes in computer Science/Lecture notes in artificial intelligence, vol 7637. Springer, Berlin, pp 310–319

  42. Parpinelli RS, Lopes HS (2012) Population resizing using nonlinear dynamics in an ecology-based approach. In: Yin H, Costa JAF, Barreto GDA (eds) Proceedings of the intelligent data engineering and automated learning, Lecture notes in computer science, vol 7435. Springer, Berlin, pp 27–34

  43. Parpinelli RS, Lopes HS (2013) An ecology-based evolutionary algorithm applied to the 2D-AB off-lattice protein structure prediction problem. In: Proceedings of the 2nd Brazilian Conference on Intelligent Systems. Conference Publishing Services

  44. Pasti R, de Castro LN, von Zuben FJ (2010) Computação biogeográfica: Formalização e proposição de uma meta-heurística espaço-temporal. Anais do XVIII Congresso Brasileiro de Automática pp 5065–5072

  45. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  46. Storn R, Price K (1997) Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MATH  MathSciNet  Google Scholar 

  47. Tonda A, Lutton E, Squillero G (2012) A benchmark for cooperative coevolution. Memet Comput 4(4):263–277

    Article  Google Scholar 

  48. Vulli SS, Agarwal S (2008) Individual-based artificial ecosystems for design and optimization. In: Keijzer M (ed) Proceedings of the conference on genetic and evolutionary computation. ACM, Atlanta, pp 273–280

    Google Scholar 

  49. Wang Y, Wang T, Tang L, Zhong D (2007) Ecosystem model based grid resource optimization management. Int J Comput Sci Netw Secur 7(2):61–66

    Google Scholar 

  50. Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  51. Yang XS, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate Program in Applied Computing, State University of Santa Catarina, Joinville, Brazil

    Rafael Stubs Parpinelli

  2. Bioinformatics Laboratory, Federal Technological University of Paraná, Curitiba, Brazil

    Heitor Silvério Lopes

Authors
  1. Rafael Stubs Parpinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heitor Silvério Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Stubs Parpinelli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parpinelli, R.S., Lopes, H.S. A computational ecosystem for optimization: review and perspectives for future research. Memetic Comp. 7, 29–41 (2015). https://doi.org/10.1007/s12293-014-0148-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-014-0148-4

Keywords

Search

Navigation