Comparison of linear and classical velocity update rules in particle swarm optimization: notes on scale and frame invariance
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[1] N. Metropolis,et al. The Monte Carlo method. , 1949 .
[2] Athanasios Papoulis,et al. Probability, Random Variables and Stochastic Processes , 1965 .
[3] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
[4] R. Salomon. Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.
[5] Ralf Salomon,et al. Some Comments on Evolutionary Algorithm Theory , 1996, Evolutionary Computation.
[6] Yue Shi,et al. A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[7] Alan D. George,et al. SCALE-INDEPENDENT BIOMECHANICAL OPTIMIZATION , 2003 .
[8] Cleve B. Moler,et al. Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later , 1978, SIAM Rev..
[9] Andries Petrus Engelbrecht,et al. A new particle swarm optimiser for linearly constrained optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[10] José Neves,et al. The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.
[11] Jan A Snyman,et al. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms , 2005 .