Particle Swarm Optimizers with Growing Tree Topology

This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.

[1]  Toshimichi Saito,et al.  Application of particle swarm optimizers to two-objective problems in design of switching inverters , 2009, 2009 International Joint Conference on Neural Networks.

[2]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[3]  Tsung-Ying Sun,et al.  Effective Learning Rate Adjustment of Blind Source Separation Based on an Improved Particle Swarm Optimizer , 2008, IEEE Transactions on Evolutionary Computation.

[4]  Riccardo Poli,et al.  Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms , 2006, IEEE Transactions on Evolutionary Computation.

[5]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Toshimichi Saito,et al.  Particle swarm optimizers with grow-and-reduce structure , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[7]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[8]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[9]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[10]  Tim M. Blackwell,et al.  The Lévy Particle Swarm , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[13]  Andries Petrus Engelbrecht,et al.  Determining RNA Secondary Structure using Set-based Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  A. Engelbrecht,et al.  Using vector operations to identify niches for particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[15]  Toshio Fukuda,et al.  A PSO-based Mobile Sensor Network for Odor Source Localization in Dynamic Environment: Theory, Simulation and Measurement , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[17]  R. Brits,et al.  Solving systems of unconstrained equations using particle swarm optimization , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[18]  Yuping Wang,et al.  An Evolutionary Algorithm for Global Optimization Based on Level-Set Evolution and Latin Squares , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Masafumi Hagiwara,et al.  Balancing Exploitation and Exploration in Particle Swarm Optimization: Velocity-based Reinitialization , 2008 .

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

[21]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[22]  José Valente de Oliveira,et al.  Particle swarm optimization applied to the chess game , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[23]  Alexandre R. S. Romariz,et al.  Digital filter arbitrary magnitude and phase approximations - statistical analysis applied to a stochastic-based optimization approach , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[24]  Andries P. Engelbrecht,et al.  Image Classification using Particle Swarm Optimization , 2002, SEAL.