Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point

This paper proposes a novel variant of quantum-behaved particle swarm optimization (QPSO) algorithm with the local attractor point subject to a Gaussian probability distribution (GAQPSO). The local attractor point in QPSO plays an important in that determining the convergence behavior of an individual particle. As such, the mean value and standard deviation of the proposed Gaussian probability distribution in GAQPSO are carefully selected. The distributions and diversities of the local attractor points in GAQPSO and QPSO are evaluated and compared. For the purpose of comparison, two variants of the GAQPSO algorithm are proposed by using a mutation probability and other types of probability distribution. The GAQPSO has been comprehensively evaluated on the suite of CEC2005 benchmark functions, and the experimental results are compared with those of the PSO and QPSO algorithms based on different probability distributions. It is shown by the results that the GAQPSO algorithm is an effective approach that can improve the QPSO performance considerably, that is, the GAQPSO algorithm is less likely to be stuck in local optima and hence it can achieve better solutions in most cases.

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

[2]  Jun Sun,et al.  ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM , 2009 .

[3]  Jing Liu,et al.  Quantum-behaved particle swarm optimization with mutation operator , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[4]  Wenbo Xu,et al.  Adaptive parameter control for quantum-behaved particle swarm optimization on individual level , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[5]  Xiaojun Wu,et al.  Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection , 2012, Evolutionary Computation.

[6]  Hitoshi Iba,et al.  Particle swarm optimization with Gaussian mutation , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  Weiwei Hu,et al.  A New QPSO Based BP Neural Network for Face Detection , 2007, ICFIE.

[8]  L. Coelho,et al.  Predictive Controller Tuning Using Modified Particle Swarm Optimization Based on Cauchy and Gaussian Distributions , 2005 .

[10]  L. Coelho.  A quantum particle swarm optimizer with chaotic mutation operator , 2008 .

[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]  Leandro dos Santos Coelho,et al.  PSO-E: Particle Swarm with Exponential Distribution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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

[14]  P. Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real- Parameter Optimization , 2010 .

[15]  Lehrstuhl für Elektrische,et al.  Gaussian swarm: a novel particle swarm optimization algorithm , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[16]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

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

[18]  L. Coelho.  Novel Gaussian quantum-behaved particle swarm optimiser applied to electromagnetic design , 2007 .

[19]  Nadia Nedjah,et al.  Gaussian Quantum-Behaved Particle Swarm Optimization Applied to Fuzzy PID Controller Design , 2008, Quantum Inspired Intelligent Systems.

[20]  Xiao-Feng Xie,et al.  Adaptive particle swarm optimization on individual level , 2002, 6th International Conference on Signal Processing, 2002..

[21]  Renato A. Krohling,et al.  Bare Bones Particle Swarm Optimization with Gaussian or Cauchy jumps , 2009, 2009 IEEE Congress on Evolutionary Computation.

[22]  James Kennedy,et al.  Dynamic-probabilistic particle swarms , 2005, GECCO '05.

[23]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[24]  Jing Liu,et al.  Quantum-Behaved Particle Swarm Optimization with Adaptive Mutation Operator , 2006, ICNC.

[25]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[26]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

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

[28]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[29]  Riccardo Poli,et al.  Analysis of the publications on the applications of particle swarm optimisation , 2008 .

[30]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[31]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[32]  Paul S. Andrews,et al.  An Investigation into Mutation Operators for Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[33]  S. N. Omkar,et al.  Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures , 2009, Expert Syst. Appl..

[34]  Ke Chen,et al.  Applied Mathematics and Computation , 2022 .

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

[36]  A.A. Kishk,et al.  Quantum Particle Swarm Optimization for Electromagnetics , 2006, IEEE Transactions on Antennas and Propagation.

[37]  P. Alotto,et al.  Global Optimization of Electromagnetic Devices Using an Exponential Quantum-Behaved Particle Swarm Optimizer , 2008, IEEE Transactions on Magnetics.

[38]  James Kennedy,et al.  Probability and dynamics in the particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[39]  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).

[40]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[41]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[42]  Renato A. Krohling,et al.  Gaussian particle swarm with jumps , 2005, 2005 IEEE Congress on Evolutionary Computation.

[43]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).