Comparison of linear and classical velocity update rules in particle swarm optimization: Notes on diversity

International Journal for Numerical Methods in Engineering

Volumn 70, Issue 8, 2007, Pages 962-984

  Wilke, Daniel N ^a   Kok, Schalk ^a   Groenwold, Albert A ^b  

^a UNIVERSITY OF PRETORIA   (South Africa)

^b STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

Classical; Directional diversity; Instantaneous search domain; Limit behaviour; Line search; Linear; Magnitude diversity; Particle swarm optimization; Trajectory collapse

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; OPTIMIZATION; RANDOM PROCESSES;

DIRECTIONAL DIVERSITY; PARTICLE SWARM OPTIMIZATION; STOCHASTIC SEARCH TRAJECTORIES; VELOCITY UPDATE RULE;

ALGORITHMS;

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; OPTIMIZATION; RANDOM PROCESSES;

EID: 34249309600     PISSN: 00295981     EISSN: 10970207     Source Type: Journal    
DOI: 10.1002/nme.1867     Document Type: Article

References (35)

1. Solis FJ, Wets RJ. Minimization by random search techniques. Mathematics of Operations Research 1981; 6:19-30.
2. De Jong KA. An analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan, Ann Arbour, MI, 1975.
3. Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 1, fundamentals. University of Wales, College of Cardiff; University Computing 1993; 15:58-69.
4. Rechenberg I. Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog: Stuttgart, 1973.
5. Storn R, Price K. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997; 11:341-359.
6. Kennedy J, Eberhart RC. Particle swarm optimization. IEEE International Conference on Neural Networks 1995; 4:1942-1948.
7. Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. Sixth International Symposium on Micro Machine and Human Science 1995; 39-43.
8. Kennedy J, Eberhart RC. A discrete binary version of the particle swarm algorithm. IEEE Conference on Systems, Man, and Cybernetics 1997; 5:4104-4108.
9. Chang RF, Lu CN. Feeder reconfiguration for load factor improvement. IEEE Conference on Power Engineering Society Transmission and Distribution 2002; 980-984.
10. Yang S, Wang M, Jiao L. A quantum particle swarm optimization. IEEE Congress on Evolutionary Computation 2004; 1:320-324.
11. Kennedy J. Bare bones particle swarms. IEEE Swarm Intelligence Symposium 2003; 80-87.
12. Secrest BR, Lamont GB. Visualizing particle swarm optimization - Gaussian particle swarm optimization. IEEE Swarm Intelligence Symposium 2003; 198-204.
13. Ozcan E, Mohan CK. Particle swarm optimization: surfing the waves. IEEE Congress on Evolutionary Computation 1999; 3:1939-1944.
14. Clerc M, Kennedy J. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 2002; 6:58-73.
15. Trelea IC. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 2003; 85:317-325.
16. Paquet U, Engelbrecht AP. A new particle swarm optimiser for linearly constrained optimisation. IEEE Congress on Evolutionary Computation 2003; 1:227-233.
17. Wilke DN, Kok S, Groenwold AA. Comparison of linear and classical velocity update rules in particle swarm optimization: notes on scale and frame invariance. International Journal for Numerical Methods in Engineering, in this issue.
18. Shi Y, Eberhart RC. A modified particle swarm optimizer. IEEE Conference on Evolutionary Computation 1998; 69-73.
19. Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. Evolutionary Programming VII 1998; 591-600.
20. Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation 2000; 1:84-88.
21. Slade WH, Ressom H, Musavi MT, Miller RL. Inversion of ocean color observations using particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing 2004; 42:1915-1923.
22. Carlisle A, Dozier G. An off-the-shelf PSO. Workshop on Particle Swarm Optimization 2001; 1-6.
23. van den Bergh F, Engelbrecht AP. A Cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 2004; 8:225-239.
24. Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. IEEE Congress on Evolutionary Computation 1999; 3:1951-1957.
25. Pees ES, van den Bergh F, Engelbrecht AP. Using neighbourhoods with guaranteed convergence PSO. IEEE Swarm Intelligence Symposium 2003; 235-242.
26. Hu X, Shi Y, Eberhart R. Recent advances in particle swarm. IEEE Congress on Evolutionary Computation 2004; 1:90-97.
27. Xie X-F, Zhang W-J, Yang Z-L. Hybrid particle swarm optimizer with mass extinction. IEEE Conference on Communications, Circuits and Systems and West Sino Expositions 2002; 2:1170-1173.
28. Ho SL, Yang S, Ni G, Lo EWC, Wong HC. A particle swarm optimization-based method for multiobjective design optimizations. IEEE Transactions on Magnetics 2005; 41(5):1756-1759.
29. Venter G, Sobieszczanski-Sobieski J. Particle swarm optimization. 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 2002; Paper AIAA-2002-1235.
30. Kennedy J. Stereotyping: improving particle swarm performance with cluster analysis. IEEE Congress on Evolutionary Computation 2000; 2:1507-1512.
31. Peram T, Veeramachaneni K, Mohan CK. Fitness-distance-ratio based particle swarm optimization. IEEE Swarm Intelligence Symposium 2003; 174-181.
32. Baskar S, Suganthan PN. A novel concurrent particle swarm optimization. IEEE Congress on Evolutionary Computation 2004; 1:792-796.
33. Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation 2004; 8:204-210.
34. Zheng Y-L, Ma L-H, Zhang L-Y, Qian J-X. On the convergence analysis and parameter selection in particle swarm optimization. IEEE Conference on Machine Learning and Cybernetics 2003; 3:1802-1807.
35. Brandstätter B, Baumgartner U. Particle swarm optimization - mass-spring system analogon. IEEE Transactions on Magnetics 2002; 38:997-1000.