Clan particle swarm optimization

Purpose – Particle swarm optimization (PSO) has been used to solve many different types of optimization problems. In spite of this, the original version of PSO is not capable to find reasonable solutions for some types of problems. Therefore, novel approaches to deal with more sophisticated problems are required. Many variations of the basic PSO form have been explored, targeting the velocity update equation. Other approaches attempt to change the communication topology inside the swarm. The purpose of this paper is to propose a topology based on the concept of clans.Design/methodology/approach – First of all, this paper presents a detailed description of its proposal. After that, it shows a graphical convergence analysis for the Rosenbrock benchmark function. In the sequence, a convergence analysis for clan PSO with different parameters is performed. A comparison with star, ring, focal, von Neumann and four clusters topologies is also performed.Findings – The paper's simulation results have shown that th...

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

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

[3]  Andries Petrus Engelbrecht,et al.  Using neighbourhoods with the guaranteed convergence PSO , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[4]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[5]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  José Neves,et al.  Watch thy neighbor or how the swarm can learn from its environment , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[8]  Suganthan.  [IEEE 1999. Congress on Evolutionary Computation-CEC99 - Washington, DC, USA (6-9 July 1999)] Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) - Particle swarm optimiser with neighbourhood operator , 1999 .

[9]  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).

[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]  Carlos A. Coello Coello,et al.  Surrogate-based Multi-Objective Particle Swarm Optimization , 2008, 2008 IEEE Swarm Intelligence Symposium.

[12]  F. van den Bergh,et al.  Training product unit networks using cooperative particle swarm optimisers , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[13]  Andrew M. Sutton,et al.  PSO and multi-funnel landscapes: how cooperation might limit exploration , 2006, GECCO.

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

[15]  Ying Tan,et al.  Dispersed particle swarm optimization , 2008, Inf. Process. Lett..

[16]  Paulo Cortez,et al.  Particle swarms for feedforward neural network training , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[17]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[18]  Mahamed G. H. Omran,et al.  Barebones particle swarm methods for unsupervised image classification , 2007, 2007 IEEE Congress on Evolutionary Computation.

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

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

[21]  Alex Alves Freitas,et al.  A hybrid PSO/ACO algorithm for classification , 2007, GECCO '07.

[22]  Xingsheng Gu,et al.  A dynamic inertia weight particle swarm optimization algorithm , 2008 .

[23]  James Kennedy,et al.  Why does it need velocity? , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[24]  Roberto Battiti,et al.  The gregarious particle swarm optimizer (G-PSO) , 2006, GECCO '06.

[25]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

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