Psychological model of particle swarm optimization based multiple emotions

Applied Intelligence

Volumn 36, Issue 3, 2012, Pages 649-663

  Ben Ali, Yamina Mohamed ^a  

^a BADJI MOKHTAR UNIVERSITY   (Algeria)

Author keywords

Emotional PSO; Psychological model; Semi adaptive inertia weight; Swarm particle optimization

Indexed keywords

BEST POSITION; EMOTIONAL BEHAVIOR; EMOTIONAL FACTORS; EMOTIONAL PSO; INERTIA WEIGHT; MULTIDIMENSIONAL SEARCH; PARTICLE OPTIMIZATION; POTENTIAL PARAMETERS; POTENTIAL REGION; PSYCHOLOGICAL MODEL; REAL OPTIMIZATION PROBLEMS; SEARCH SPACES; SELF ADAPTIVE CONTROL; SUB-SWARMS;

ALGORITHMS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84862127069     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-011-0282-3     Document Type: Article

References (29)

1. Angeline P (1998a) Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In: Proceedings of the seventh annual conference on evolutionary programming, pp 601-610 (Pubitemid 128118970)
2. Angeline P (1998b) Using selection to improve particle swarm optimization. In: Proceedings of the IEEE conference on evolutionary computation, pp 84-89
3. Auger A, Kern S, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: IEEE congress on evolutionary computation, pp 1769-1776 (Pubitemid 41496063)
4. Clerc M (1999) The swarm and the Queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the congress on evolutionary computation, vol 3. IEEE Press, New York, pp 1951-1957
5. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimentional complex space. IEEE Trans Evol Comput 6(1):58-73 (Pubitemid 34404564)
6. Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 84-88
7. Esquivel SC, Coello Coello CA (2003) On the use of particle swarm optimization with multi modal functions. In: Proceedings of the congress on evolutionary computation, pp 1130-1136
8. Ge Y, Rubo Z (2005) AN emotional particle swarm algorithm. In: Advances in natural computation. LNCS, vol 3612, pp 553-561
9. Guidelines (2005). http://www.ntu.edu.sg/home/EPNSugan
10. Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of the IEEE swarm intelligence symposium, pp 72-79
11. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proc IEEE international conference on neural networks, Piscat-away, NJ, pp 1942-1948
12. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann, San Mateo
13. Krink T, Lovbjerg M (2002a) The lifecycle model: combining particle swarm optimization, genetic algorithms and hill climbing. In: Proceedings of parallel problem solving from nature, pp 621-630
14. Krink T, Vesterstrøm JS, Riget J (2002b) Particle swarm optimization with spatial particle extension. In: Proceedings of the fourth congress on evolutionary computation
15. Lovbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimizer with breeding and subpopulation. In: Proceedings of the third conference on genetic and evolutionary computation, pp 469-476
16. Monson CK, Seppi KD (2006) Adaptive diversity in PSO. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, Seattle, Washington, USA, pp 59-66 (Pubitemid 44611295)
17. Li N, Qin Y-Q, Sun D-B, Tong Z (2004) Particle swarm optimization with mutation operator. In: Proc international conference on machine learning and cybernetics, vol 4, pp 2251-2256
18. Ozcan E, Mohan CK (1998) Analysis of a simple particle swarm optimization system. In: Intelligent engineering systems through artificial neural networks, pp 253-258 (Pubitemid 128724535)
19. Ozcan E, Mohan CK (1999) Particle swarm optimization: surfing the waves. In: Proceedings of the IEEE congress on evolutionary computation, Washington, DC, USA
20. Ratnaweera A, Halgamuge S, Watson H (2002) Particle swarm optimization with self-adaptive acceleration coefficients. In: Proceedings of the 1st international conference on fuzzy systems and knowledge discovery, pp 264-268
21. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proc IEEE congress on evolutionary computation, pp 69-73
22. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proc IEEE congress on evolutionary computation, pp 1945-1950
23. Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proc IEEE congress on evolutionary computation, pp 101-106 (Pubitemid 32806325)
24. Stacey A, Jancic M, Grundy I (2003) Particle swarm optimization. In: Proceedings of the congress on evolutionary computation, pp 1425-1430
25. Van den Bergh F, Engelbrecht AP (2004) A comparative approach to particle swarm optimization. IEEE Trans Evol Comput 8:225-239
26. Van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937-971 (Pubitemid 43190068)
27. Wukmir WJ (1967) Emoción y Sufrimiento. BUL Labor, Barcelona
28. Wang W, Wang Z, Gu X, Zheng S (2009) Emotional particle swarm optimization. In: International conference on emerging intelligent computing technology and applications. LNCS, vol 575, pp 766-775
29. Zhang W, Xie X (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: IEEE international conference on systems, man and cybernetics, Washington, pp 3816-3821