Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution

Applied Intelligence

Volumn 42, Issue 3, 2015, Pages 514-526

  Chen, Stephen ^a   Montgomery, James ^b   Bolufé Röhler, Antonio ^c  

^a YORK UNIVERSITY   (Canada)

^b UNIVERSITY OF TASMANIA   (Australia)

^c UNIVERSITY OF HAVANA   (Cuba)

Author keywords

Curse of dimensionality; Differential evolution; Exploitation; Exploration; Large scale global optimization; Particle swarm optimization

Indexed keywords

BUDGET CONTROL; GLOBAL OPTIMIZATION; NATURAL RESOURCES EXPLORATION; POPULATION STATISTICS;

CONTINUOUS DOMAIN; CURSE OF DIMENSIONALITY; DIFFERENTIAL EVOLUTION; EXPLOITATION; HIGH-DIMENSIONAL; LARGE SCALE GLOBAL OPTIMIZATIONS; POPULATION SIZES; SEARCH CHARACTERISTICS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84925537862     PISSN: 0924669X     EISSN: 15737497     Source Type: Journal    
DOI: 10.1007/s10489-014-0613-2     Document Type: Article

References (29)

1. Kennedy J, Eberhart RC (1995) Particle swarm optimization. IEEE ICNN:1942–1948
2. Storn R (1996) On the usage of differential evolution for function optimization. EEE NAFIPS:519–523
3. Bellman RE (1957) Dynamic Programming. Princeton University Press, Princeton
4. Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. IEEE SIS:120–127
5. Chu W, Gao X, Sorooshian S (2011) A new evolutionary search strategy for global optimization of high-dimensional problems. Inform Sci 181:4909–4927
6. Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE SMC 16(1):122–128
7. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. IEEE CEC:1945–1950
8. Zhang L-P, Yu H-J, Hu S-X (2005) Optimal choice of parameters for particle swarm optimization. J Zhejiang Univ Sci 2005 6A(6):528–534
9. Mallipeddi R, Suganthan PN (2008) Empirical study on the effect of population size on differential evolution algorithm. IEEE CEC:3663–3670
10. Dragoi E-N, Curteanu S, Leon F, Galaction A-I, Cascaval D (2011) Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm. Eng Appl AI 24(7):1214–1226
11. Vafashoar R, Meybodi MR, Momeni Azandaryani AH (2012) CLA-DE: a hybrid model based on cellular learning automata for numerical optimization. Appl Intell 36(3):735–748
12. Hansen N, Finck S, Ros R, Auger A (2009) Real-parameter black-box optimization benchmarking 2009: noiseless functions definitions, INRIA Technical Report RR-6829
13. Liang JJ, Qu BY, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Technical Report. Nanyang Technological University
14. Engelbrecht A (2012) Particle swarm optimization: velocity initialization. IEEE CEC:70–77
15. Helwig S, Branke J, Mostaghim S (2013) Experimental analysis of bound handling techniques in particle swarm optimization. IEEE TEC 17 (2):259–271
16. https://www.researchgate.net/publication/259643342_Source_code_for_an_implementation_of_Standard_Particle_Swarm_Optimization_--_revised?ev=prf_pub
17. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
18. Montgomery J, Chen S (2010) An analysis of the operation of differential evolution at high and low crossover rates. IEEE CEC:881–888
19. Montgomery J (2009) Differential evolution: Difference vectors and movement in solution space. IEEE CEC:2833–2840
20. Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. IEEE CEC:1951–1957
21. Jin’no K, Shindo T. (2010) Analysis of dynamical characteristic of canonical deterministic PSO. IEEE CEC:1105–1110
22. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313
23. Singer S, Singer S (1999) Complexity analysis of Nelder-Mead search iterations. Proc Appl Math Comput:185–196
24. Pham N, Wilamowski BM (2011) Improved Nelder Mead’s simplex method and applications. J Comput 3(3):55–63
25. Gao F., Han L (2012) Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Comput Optim Appl 51(1):259–277
26. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
27. Dueck G, Scheuer T (1990) Threshold accepting: A general purpose optimization algorithm appearing superior to simulated annealing. J Comp Phys 90(1):161–175
28. Talbi E-G (2009) Metaheuristics. Wiley Publishing, From Design to Implementation
29. (2009) Particle swarm optimisation and high dimensional problem spaces. IEEE CEC:1988–1994