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Abstract: Particle Swarm Optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-Organized Criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions. (Update)
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BibTeX entry: (Update)
@misc{ vbjerg-extending,
author = "Morten Løvbjerg and T. Krink",
title = "Extending Particle Swarm Opimisers with Self-Organized Criticality",
url = "citeseer.ist.psu.edu/530204.html" }
Citations (may not include all citations):63 Particle swarm opti- mization (context) - Kennedy, Eberhart - 1995
36 Parameter Selection in Par- ticle Swarm Optimization (context) - Shi, Eberhart - 1998
29 Genetic Algorithms for changing envi- ronments - Grefenstette - 1992
28 How Nature Works (context) - Bak - 1996
20 Comparison between Genetic Algorithms and Particle Swarm Opt.. (context) - Eberhart, Shi - 1998 ACM DBLP
18 Evolutionary Optimization Versus Par- ticle Swarm Optimizati.. (context) - Angeline - 1998
14 Empirical Study of Particle Swarm Optimization (context) - Shi, Eberhart - 1999
11 Applying Self- Organised Criticality to Evolutionary Algorit.. - Krink, Thomsen et al. - 2000
9 The Swarm and the Queen: Towards a Deterministic and Adaptiv.. (context) - Clerc - 1999
6 Particle Swarm Optimiser with Neigh- bourhood Operator (context) - Suganthan - 1999
3 Small Worlds and Mega-Minds: Effects of Neighborhood Topolog.. (context) - Kennedy - 1999
1 Applying Self- Organized Criticality to the Diffusion Model (context) - Rickets, Thomsen et al. - 2000
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