Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies

Quantum-behaved particle swarm optimization (QPSO), motivated by analysis from particle swarm optimization (PSO) and quantum mechanics, has shown excellent performance in finding the optimal solutions for many optimization problems. In QPSO, the mean best position, defined as the average of the personal best positions of all the particles in a swarm, is employed as a global attractor to attract the particles to search solutions globally. This paper presents a comprehensive analysis of the mean best position and proposes several novel adaptive strategies to determine the position. In particular, four variants of mean best position are proposed to serve as global attractors and the corresponding parameter selection methods are also provided. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other QPSO and PSO variants. The simulation results show that the proposed QPSO algorithm have some advantages over the original QPSO and other PSO algorithms.

[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]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Wenbo Xu,et al.  An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position , 2008, Appl. Math. Comput..

[5]  Xiaojun Wu,et al.  Convergence analysis and improvements of quantum-behaved particle swarm optimization , 2012, Inf. Sci..

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

[7]  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.

[8]  Wenbo Xu,et al.  A Diversity-Guided Quantum-Behaved Particle Swarm Optimization Algorithm , 2006, SEAL.

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

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

[11]  Hirosato Nomura,et al.  Quantum-Behaved Particle Swarm Optimization with Chaotic Search , 2008, IEICE Trans. Inf. Syst..

[12]  W R.,et al.  Comparisons of Treatments After an Analysis of Variance in Ecology , 2007 .

[13]  Wenbo Xu,et al.  A Cooperative Approach to Quantum-behaved Particle Swarm Optimization , 2007, 2007 IEEE International Symposium on Intelligent Signal Processing.

[14]  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).

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

[16]  Gary B. Lamont,et al.  Visualizing particle swarm optimization - Gaussian particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[17]  J. Kennedy,et al.  Stereotyping: improving particle swarm performance with cluster analysis , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[18]  Xiaojun Wu,et al.  Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point , 2011, Appl. Math. Comput..

[19]  Xu Wen-bo.  Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Differential Evolution Operator and Its Application , 2008 .

[20]  Xu Wen-bo.  Parameter selection of quantum-behaved particle swarm optimization , 2007 .

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

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

[23]  Fábio Lúcio Santos,et al.  Simplified particle swarm optimization algorithm , 2012 .

[24]  Wenbo Xu,et al.  Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity , 2006, International Conference on Computational Science.

[25]  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).

[26]  Mohammad Mehdi Ebadzadeh,et al.  Dynamic Particle Swarm Optimization for Multimodal Function , 2012 .

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

[28]  K. C. Tan,et al.  Continuous Optimization A competitive and cooperative coevolutionary approach to multi-objective particle swarm optimization algorithm design , 2009 .

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

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

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

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

[33]  Songfeng Lu,et al.  Coevolutionary Quantum-Behaved Particle Swarm Optimization with Hybrid Cooperative Search , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[34]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[35]  James Kennedy.  In Search of the Essential Particle Swarm , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[36]  Xiaojun Wu,et al.  A Review of Quantum-behaved Particle Swarm Optimization , 2010 .

[37]  Christian Posthoff,et al.  Neighborhood Re-structuring in Particle Swarm Optimization , 2005, Australian Conference on Artificial Intelligence.