Efficient multi-swarm PSO algorithms for dynamic environments

Memetic Computing

Volumn 3, Issue 3, 2011, Pages 163-174

  Novoa Hernández, Pavel ^a   Corona, Carlos Cruz ^b   Pelta, David A ^b  

^a UNIVERSIDAD DE HOLGUÍN   (Cuba)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Dynamic environments; Fuzzy rule; Multi swarm PSO

Indexed keywords

DYNAMIC ENVIRONMENTS; DYNAMIC OPTIMIZATION PROBLEMS; EXPERIMENTAL ANALYSIS; MULTI-MODAL; MULTI-SWARMS; PSO ALGORITHMS; RESEARCH AND APPLICATION;

ALGORITHMS; FUZZY RULES;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 80052837212     PISSN: 18659284     EISSN: 18659292     Source Type: Journal    
DOI: 10.1007/s12293-011-0066-7     Document Type: Article

References (32)

1. Angeline P (1997) Tracking extrema in dynamic environments. In: Angeline P, Reynolds R, McDonnell J, Eberhart R (eds) Evolutionary programming VI, Lecture notes in computer science, vol 1213. Springer, Berlin, pp 335-345.
2. Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. Part I: background and development. Nat Comput Int J 6: 467-848.
3. Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput Int J 7: 109-124.
4. Blackwell T (2005) Particle swarms and population diversity. Soft Comput Fusion Found Methodol Appl 9: 793-802.
5. Blackwell T, Bentley P (2002) Don't push me! collision-avoiding swarms. In: CEC '02: Proceedings the IEEE congress on evolutionary computation. IEEE Computer Society, Washington, DC, USA, pp 1691-1696.
6. Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. In: Lecture notes in computer science, vol 3005. Springer, Heidelberg, pp 489-500.
7. Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4): 459-472.
8. Blackwell T, Branke J, Li X (2008) Particle swarms for dynamic optimization problems. In: Rozenberg GEA (eds) Swarm intelligence, natural computing series. Springer, Berlin, pp 193-217.
9. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: CEC 99. Proceedings of the congress on evolutionary computation, vol 3. IEEE Press, pp 1875-1882.
10. Branke J, Schmeck H (2002) Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui S, Ghosh A (eds) Theory and application of evolutionary computation: recent trends. Springer, Berlin, pp 239-262.
11. Clerc M (2006) Particle swarm optimization. Wiley-ISTE, New York.
12. lerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1): 58-73.
13. Cruz C, González J, Pelta D (2010) Optimization in dynamic environments: a survey on problems, methods and measures. In: Soft computing-a fusion of foundations, methodologies and applications, pp 1-22.
14. Dasgupta D, Mcgregor D (1992) Nonstationary function optimization using the structured genetic algorithm. In: Parallel problem solving from nature. Elsevier, Amsterdam, pp 145-154.
15. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science MHS95. IEEE Press, pp 39-43.
16. García Del Amo I, Pelta D, González J, Novoa P (2010) An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems. In: Current topics in artificial intelligence. Lecture notes in computer science, vol 5988, pp 32-41.
17. Gonzalez J, Masegosa A, Garcia I (2011) A cooperative strategy for solving dynamic optimization problems. Memetic Comput 3: 3-14.
18. Hu X, Eberhart R (2002) Adaptive particle swarm optimization: Detection and response to dynamic systems. In: CEC '02: proceedings of the IEEE congress on evolutionary computation. IEE Press, pp 1666-1670.
19. Janson S, Middendorf M (2004) A hierarchical particle swarm optimizer for dynamic optimization problems. In: Lecture notes in computer science, vol 3005. Springer, Berlin, pp 513-524.
20. Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3): 303-317.
21. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, pp 1942-1948.
22. Li C, Yang S (2008) A generalized approach to construct benchmark problems for dynamic optimization. In: Simulated evolution and learning. Lecture notes in computer science, vol 5361. Springer, Berlin, pp 391-400.
23. Lung R, Dumitrescu D (2010) Evolutionary swarm cooperative optimization in dynamic environments. Nat Comput Int J 9(1): 83-94.
24. Morrison R, De Jong K (1999) A test problem generator for non-stationary environments. In: Proceedings of the 1999 congress on evolutionary computation, vol 3, pp 2047-2053.
25. Moser I, Chiong R (2010) Dynamic function optimisation with hybridised extremal dynamics. Memetic Comput 2: 137-148.
26. Novoa-Hernández P, Pelta D, Corona C (2010) Improvement strategies for multi-swarm pso in dynamic environments. In: González J, Pelta D, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, pp 371-383.
27. Parrott D, Li X (2004) A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: CEC '04: proceedings the IEEE congress on evolutionary computation, vol 1, pp 98-103.
28. Pelta D, Cruz C, Verdegay J (2009) Simple control rules in a cooperative system for dynamic optimization problems. Int J Gen Syst 38(7): 701-717.
29. Pelta D, Sancho-Royo A, Cruz C, Verdegay JL (2006) Memory and fuzzy rules in a co-operative multi-thread strategy for optimization. Inform Sci 176(13): 1849-1868.
30. Wang H, Yang S, Ip W, Wang D (2010) A particle swarm optimization based memetic algorithm for dynamic optimization problems. Nat Comput Int J 9(3): 703-725.
31. Weicker K (2002) Performance measures for dynamic environments. In: Guervós JJM, Adamidis P, Beyer HG, Fernández-Villacãnas Marti{dotless} JL, Schwefel HP (eds) Parallel problem solving from nature, vol VII. Springer, Berlin, pp 64-73.
32. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3: 82-102.