A novel memetic algorithm and its application to data clustering

Memetic Computing

Volumn 5, Issue 1, 2013, Pages 65-78

  Ni, JiaCheng ^a   Li, Li ^a   Qiao, Fei ^a   Wu, QiDi ^a  

^a TONGJI UNIVERSITY   (China)

Author keywords

Gaussian mutation; Memetic algorithm; PSO; SA based local search

Indexed keywords

BENCHMARKING; CLUSTER ANALYSIS; GAUSSIAN DISTRIBUTION; GENETIC ALGORITHMS; LOCAL SEARCH (OPTIMIZATION); PARTICLE SWARM OPTIMIZATION (PSO); SIMULATED ANNEALING;

BENCHMARK FUNCTIONS; COMPUTING RESOURCE; GAUSSIAN MUTATION; LOCAL SEARCH; LOCAL SEARCH OPERATORS; LOCAL SEARCH STRATEGY; MEMETIC ALGORITHMS; OPTIMIZATION ABILITY;

CLUSTERING ALGORITHMS;

EID: 84874191949     PISSN: 18659284     EISSN: 18659292     Source Type: Journal    
DOI: 10.1007/s12293-012-0087-x     Document Type: Article

References (32)

1. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks IV, pp 1942-1948.
2. Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Ph. D. dissertation, Univ. West of England, Bristol, UK.
3. Krasnogor N, Smith JE (2005) A tutorial for competent memetic algorithms: model, taxonomy and design isuses. IEEE Trans Evol Comput 9(5): 474-488.
4. Ong YS, Lim MH, Zhu N (2006) Classification of adaptive memetic algorithms: acomparative study. IEEE Trans Syst Man Cyber 36(1): 141-152.
5. Clerc M, Kenndey J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1): 58-73.
6. Mendes R (2004) Population topologies and their influence in particle swarm performance. PhD dissertation, University of Minho.
7. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, pp 1931-1938.
8. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, pp 1671-1676.
9. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8: 204-210.
10. Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proc. swarm intelligence symp., pp 174-181.
11. Liang JJ, Qin QK, Suganthan PN (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal funcitons. IEEE Trans Evol Comput 10(3): 281-295.
12. Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of IEEE international conference on evolutionary computation, pp 303-308.
13. Dawkins R (1976) The Selfish Gene. Oxford University Press, New York.
14. Moscato P (1989) On evolution, search, optimization, GAs and martial arts: Toward memetic algorithms. California Inst. Technol., Pasadena, CA, Tech. Rep. Caltech Concurrent Comput. Prog. Rep. 826.
15. Nguyen QH, Ong YS, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3): 604-623.
16. Ong YS, Lim MH, Chen XS (2010) Research frontier: memetic computation-past, present & future. IEEE Comput Intell Mag 5(2): 24-36.
17. Meuth R, Lim MH, Ong YS, Wunsch DC II (2009) A proposition on Memes and Meta-memes in computing for higher-order learning. Memetic Comput J 1(2): 85-100.
18. Chen XS, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on Memetic computation. IEEE Trans Evol Comput 15(5): 591-607.
19. Sun J, Garibaldi JM, Krasnogor N, Zhang Q (2012) An intelligent multi-restart memetic algorithm for box-constrained global optimisation. Posted Online February 15.
20. Hart W (1994) Adaptive global optimization with local search. Ph. d dissertations, University of California, San Diego.
21. Binkley KJ, Hagiwara M (2005) Particle swarm optimization with area of influence: increasing the effectiveness of the swarm. In: Proceedings of IEEE Swarm Intelligence Symposium, pp 45-52.
22. Das S, Koduru P, Gui M (2006) Adding local search to particle swarm optimization. In: Proceedings of the congress on evolutionary computation, pp 428-433.
23. Petalas YG, Parsopoulos KE, Vrahatis MN (2007) Memetic particle swarm optimization. Ann Oper Res 156(1): 99-127.
24. Higashi H, Iba H (2003) Particles warm optimization with Gaussian mutation. In: Proceedings of the IEEE swarm intelligence symphosium, pp 72-79.
25. Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: Proceedings of the congress on evolutionary computation, pp 1044-1051.
26. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220: 671-680.
27. Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2): 82-102.
28. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE congress on evolutionary computation, pp 69-73.
29. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1: 67-82.
30. MacQueen J JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability. University of California Press, pp 281-297.
31. Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. IEEE Congress on Evolutionary Computation, pp 215-220.
32. Pei Z, Hua X (2008) The clustering algorithm based on particle swarm optimization algorithm. In: International conference on intelligent computation technology and automation, pp 148-151.