From Theory to Practice in Particle Swarm Optimization

The purpose of this chapter is to draw attention to two points that are not always well understood, namely, a) the “balance” between exploitation and exploration may be not what we intuitively think, and b) a mean best result may be meaningless. The second point is obviously quite important when two algorithms are compared. These are discussed in the appendix. We believe that these points would be useful to researchers in the field for analysis and comparison of algorithms in a better and rigorous way, and help them design new powerful tools.

[1]  Juan Luis Fernández-Martínez,et al.  Theoretical analysis of particle swarm trajectories through a mechanical analogy , 2008 .

[2]  Tien-Tsin Wong,et al.  Sampling with Hammersley and Halton Points , 1997, J. Graphics, GPU, & Game Tools.

[3]  Rolf Wanka,et al.  Particle Swarm Optimization in High-Dimensional Bounded Search Spaces , 2007, 2007 IEEE Swarm Intelligence Symposium.

[4]  Riccardo Poli,et al.  Continuous optimisation theory made easy? finite-element models of evolutionary strategies, genetic algorithms and particle swarm optimizers , 2007, FOGA'07.

[5]  James Kenedy.  How It Works: Collaborative Trial and Error , 2008 .

[6]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[7]  Simon M. Lucas,et al.  Parallel Problem Solving from Nature - PPSN X, 10th International Conference Dortmund, Germany, September 13-17, 2008, Proceedings , 2008, PPSN.

[8]  Louis Gacôgne.  Steady state evolutionary algorithm with an operator family , 2002 .

[9]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[10]  Sanjoy Das,et al.  A Particle Swarm Optimization-Nelder Mead Hybrid Algorithm for Balanced Exploration and Exploitation in Multidimensional Search Space , 2006, IC-AI.

[11]  Michael N. Vrahatis,et al.  Parameter selection and adaptation in Unified Particle Swarm Optimization , 2007, Math. Comput. Model..

[12]  Rolf Wanka,et al.  Theoretical Analysis of Initial Particle Swarm Behavior , 2008, PPSN.

[13]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[14]  Riccardo Poli,et al.  Evolving problems to learn about particle swarm and other optimisers , 2005, 2005 IEEE Congress on Evolutionary Computation.

[15]  M. Ben Ghalia,et al.  Particle swarm optimization with an improved exploration-exploitation balance , 2008 .

[16]  Riccardo Poli,et al.  Dynamics and stability of the sampling distribution of particle swarm optimisers via moment analysis , 2008 .

[17]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[18]  Riccardo Poli,et al.  On the moments of the sampling distribution of particle swarm optimisers , 2007, GECCO '07.

[19]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[20]  Yun Shang,et al.  A Note on the Extended Rosenbrock Function , 2006 .

[21]  M. Clerc.  Stagnation Analysis in Particle Swarm Optimisation or What Happens When Nothing Happens , 2006 .

[22]  Li Ning,et al.  An Analysis for a Particle's Trajectory of PSO Based on Difference Equation , 2006 .

[23]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[24]  Maurice Clerc,et al.  Why does it work , 2008 .