Nature-Inspired Toolbox to Design and Optimize Systems

Nature-Inspired (NI) Toolbox is a Particle Swarm Optimization (PSO) based toolbox which is developed in the MATLAB environment. It has been released under General Public License and hosted at SourceForge.net (http://sourceforge.net/projects/nitool/). The purpose of this toolbox is to facilitate the users/designers in design and optimization of their systems. This chapter discusses the fundamental concepts of PSO algorithms in the initial sections, followed by discussions and illustrations of benchmark optimization functions. Various modules of the Graphical User Interface (GUI) of NI Toolbox are explained with necessary figures and snapshots. In the ending sections, simulations results present comparative performance of various PSO models with concluding remarks.

[1]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[2]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Yanchun Liang,et al.  Particle swarm optimization-based algorithms for TSP and generalized TSP , 2007, Inf. Process. Lett..

[5]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[6]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[7]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[9]  Jeng-Shyang Pan,et al.  A Parallel Particle Swarm Optimization Algorithm with Communication Strategies , 2005, J. Inf. Sci. Eng..

[10]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  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.

[12]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

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

[14]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..