A study on diversity and cooperation in a multiagent strategy for dynamic optimization problems

International Journal of Intelligent Systems

Volumn 24, Issue 7, 2009, Pages 844-861

  Pelta, David ^a   Cruz, Carlos ^a   González, Juán Ramon ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COOPERATIVE AGENTS; DYNAMIC OPTIMIZATION PROBLEMS; MULTI-AGENT; MULTI-AGENT STRATEGY; NATURAL PROCESS; SCIENCE AND TECHNOLOGY;

EID: 67650841333     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.20363     Document Type: Article

References (40)

1. deCastro LN, Zuben FJV. Recent developmentsinbiologically inspired computing. Hershey, PA: Idea Group Publishing; 2004.
2. Engelbrecht AP. Fundamentals of computational swarm intelligence. New York: Wiley; 2006.
3. Forbes N. Imitation of life: How biology is inspiring computing. Boston, MA: MIT Press; 2005.
4. Branke J. Evolutionary optimization in dynamic enviroments. Boston, MA: Kluwer Academic; 2002.
5. Dugatkin L, Reeves H, editors. Game theory and animal behavior. New York: Oxford University Press; 2000.
6. Abraham A, Grosan C, Ramos V. Stigmergic optimization, Series Studies in computational intelligence, Vol 31. Berlin: Springer-Verlag; 2006.
7. Dorigo M, T. Stützle. Ant colony optimization. Boston, MA: MIT Press; 2004.
8. Mart́i R, editor. Handbook of metaheuristics. Boston, MA: Kluwer Academic; 2003.
9. Alba E. Parallel metaheuristics: A new class of algorithms. Hoboken, NJ: Wiley; 2005.
10. Fogel DB. Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press series on computational intelligence. Piscataway, NJ: Wiley-IEEE Press; 2005.
11. N.Krasnogor and Gustafson S. Astudy on the use of "self- generation"inmemetic algorithms. Natural Comput, 2004;3(1):53-76.
12. Hogg T, Williams CP. Solving the really hard problems with cooperative search. In: Proc of AAAI93, Menlo Park, CA; 1993. pp 231-236.
13. Huberman B. The performance of cooperative processes. Physica D 1990;42:38-47.
14. Verhoeven M, Aarts E. Parallel local search. J Heuristics 1995;1(1):43-65.
15. Pelta D, Cruz C, Sancho-Royo A, Verdegay J. Soft computing and cooperative strategies for optimization. In: Proc 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications. SMCia/05, 2005.
16. Pelta D, Cruz C, Sancho-Royo A, Verdegay J. Fuzzy sets based cooperative heuristics for solving optimization problems. In: Fuzzy applications in industrial engineering. Berlin: Springer; 2006.
17. Pelta D, Sancho-Royo A, Cruz C, Verdegay JL. Using memory and fuzzy rules in a cooperative multithread strategy for optimization. Infor Sci 2006;176(13):1849-1868.
18. Angeline PJ. Tracking extrema in dynamic environments. In: Angeline PJ, Reynolds RG, McDonnell JR, Eberhart R, editors, Proc of 6th Int Conf on Evolutionary Programming Indianapolis, IN; 1997. LNCS, Vol 1213. Berlin: Springer-Verlag. pp 335-345.
19. Dorigo M, Bonabeau E, Theraulaz G. Ant algorithms and stigmergy. Future Gen Comput Syst 2000;16(8):851-871.
20. Pettit K, Swigger E. An analysis of genetic based pattern tracking and cognitive based component tracking models of adaptation. In: Proc of National Conference on AI. San Mateo, CA: Morgan Kaufmann; 1983. pp 327-332.
21. Dasgupta D, McGregor DR. Nonstationary function optimization using the structured genetic algorithm. In Manner R, Manderick B, editors. Purrillel problem solvingfioni nutiire. Amsterdam: Elsevier; 1992. pp 145-154.
22. Goldberg D, Smith RE. Nonstationary function optimization using genetic algorithm with dominance and diploidy. In Proc. of the 2nd International Conference on Genetic Algorithms on Genetic algorithms and their application; 1987. pp 59-68. L. Erlbaum Associates Inc. USA.
23. Boese K, Kahng A, Muddu S. Ant algorithms and stigmergy. Oper Res Lett 1994;16:103- 113.
24. Branke J. Memory enhanced evolutionary algorithms for changing optimization problems. In: Angeline PJ, et al editors. Proc Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press. Vol 3, pp 1875-1882. 1999.
25. Branke J, Kauler T, Schmidt C, Schmeck H. A multi-population approach to dynamic optimization problems. In Adaptive Computing in Design and Manufacturing: Selected Papers from ACDM'00 (I.C. Parmee Editor), pp 299-308. Springer, 2000.
26. Kramer G, Gallagher JC. Improvements to the *CGA enabling online intrinsic evolution in compact EH devices. Proceedings of NASA/DoD Conference on Evolvable Hardware. pp 225-231, 2003.
27. Branke J, Schmeck H. Designing evolutionary algorithms for dynamic optimization problems. In: Advances in evolutionary computing: Theory and applications, pp 239-262. Springer-Verlag, New York.
28. Bui L, Branke J, Abbass H. Diversity as a selection pressure in dynamic environments. In: Proc of the 2005 Conf on Genetic and Evolutionary Computation; 2005. pp 1557-1558.
29. Branke J, Blackwell T. Multiswarms, exclusion, anti-convergence in dynamic environments. IEEE Trans Evol Computat 2006;10(4):459-472.
30. Janson S, Middendorf M. A hierarchical particle swarm optimizer for dynamic optimization problems. In Raidl GR, editor. Applications of evolutionary computing. LNCS Vol 3005. Berlin: Springer; 2004. pp 513-524.
31. Mendes R, Mohais A. Dynde: a differential evolution for dynamic optimization problems. In IEEE Cong on Evolutionary Computation; 2005. pp 2808-2815.
32. Moser I, Hendtlass T. A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. IEEE Congress on Evolutionary Computation, pp 252-259, IEEE Press.
33. Branke J, Schmeck H. Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui S, Ghosh A, editors. Theory and application of evolutionary computation: Recent trends. Berlin: Springer-Verlag; 2002. pp 239-262.
34. Hu X,Eberhart R. Adaptive particle swarm optimization: Detection and response to dynamic systems. In: Proc of the 2005 Congress on evolutionary computation, 2002. pp 1666-1670.
35. Blackwell TM. Swarms in dynamic environments. In: Cantú-Paz E, Foster JA, et al., editors. Genetic and evolutionary computation- GECCO-2003, Chicago; July 12-16 2003. Vol 2723 LNCS. Berlin: Springer-Verlag. pp 1-12.
36. Li X. Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Deb EAK, editor. Genetic and evolutionary computation - GECCO-2004, Springer-Verlag; 2004. pp 105-116.
37. ParrotD,LiX.A particle swarm model for tracking multiple peaks inadynamic environment using speciation. In: Proc of the Congress on Evolutionary Computation, CEC2004; 2000. pp 98-103.
38. Blackwell T, Branke J. Multi-swarm optimization in dynamic environments. In: Applications of evolutionary computing; LNCS Vol 3005 Berlin: Springer; 2004. pp 489-500.
39. Wilensky U. Netlogo. Center for connected learning and computer-based modelling., NorthWestern University, Evanston, IL; 1999.
40. Dugatkin L. Animal cooperation among unrelated individuals. Naturwissenschaften 2002;89:533-541.