Initializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method

Initialization of the population in Evolutionary Computation algorithms is an issue of ongoing research. Proper initialization may help the algorithm to explore the search space more efficiently and detect better solutions. In this paper, the Nonlinear Simplex Method is used to initialize the swarm of the Particle Swarm technique. Experiments for several well-known benchmark problems imply that better convergence rates and success rates can be achieved by initializing the swarm this way. Key-Words: Particle Swarm, Nonlinear Simplex Method, Optimization

[1]  E Bonabeau,et al.  Swarm Intelligence: A Whole New Way to Think about Business , 2001 .

[2]  Paul Schwefel.  On the Evolution of Evolutionary Computation Hans – , 1994 .

[3]  Vassilis P. Plagianakos,et al.  Improving the Particle Swarm Optimizer by Function “Stretching” , 2001 .

[4]  K. Parsopoulos,et al.  Stretching technique for obtaining global minimizers through Particle Swarm Optimization , 2001 .

[5]  Jacek M. Zurada,et al.  Computational Intelligence: Imitating Life , 1994 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Virginia Torczon,et al.  On the Convergence of the Multidirectional Search Algorithm , 1991, SIAM J. Optim..

[8]  W. Pinebrook.  The evolution of strategy. , 1990, Case studies in health administration.

[9]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[11]  Konstantinos E. Parsopoulos,et al.  PARTICLE SWARM OPTIMIZER IN NOISY AND CONTINUOUSLY CHANGING ENVIRONMENTS , 2001 .

[12]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[13]  E. O. Roxin.  Optimization Theory and Applications (Lamberto Cesari) , 1984 .

[14]  A. V. Levy,et al.  Topics in global optimization , 1982 .

[15]  Michael N. Vrahatis,et al.  Modification of the Particle Swarm Optimizer for Locating All the Global Minima , 2001 .

[16]  Jorge J. Moré,et al.  Testing Unconstrained Optimization Software , 1981, TOMS.

[17]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[18]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[19]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[20]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[21]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[22]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[23]  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.

[24]  William H. Press,et al.  Numerical recipes , 1990 .