Self-adaptive Quantum Particle Swarm Optimization for Dynamic Environments

The quantum-inspired particle swarm optimization (QPSO) algorithm has been developed to find and track an optimum for dynamic optimization problems. Though QPSO has been shown to be effective, despite its simplicity, it does introduce an additional control parameter: the radius of the quantum cloud. The performance of QPSO is sensitive to the value assigned to this problem dependent parameter, which basically limits the area of the search space wherein new, better optima can be detected. This paper proposes a strategy to dynamically adapt the quantum radius, with changes in the environment. A comparison of the adaptive radius QPSO with the static radius QPSO showed that the adaptive approach achieves desirable results, without prior tuning of the quantum radius.

[1]  Andries Petrus Engelbrecht,et al.  The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization for Solving Dynamic Optimization Problems , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[2]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[3]  Gary Pamparà,et al.  CIlib v2.0.0 Milestone 1 , 2014 .

[4]  Zbigniew Michalewicz,et al.  Searching for optima in non-stationary environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  K. Deb,et al.  Real-coded evolutionary algorithms with parent-centric recombination , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Xin Yao,et al.  Benchmark Generator for CEC'2009 Competition on Dynamic Optimization , 2008 .

[7]  Peter J. Angeline,et al.  Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.

[8]  Jürgen Branke,et al.  Proceedings of the Workshop on Evolutionary Algorithms for Dynamic Optimization Problems (EvoDOP-2003) held in conjunction with the Genetic and Evolutionary Computation Conference (GECCO-2003), 12 July 2003, Chicago, USA [online] , 2003 .

[9]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Andries Petrus Engelbrecht,et al.  A radius-free quantum particle swarm optimization technique for dynamic optimization problems , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[12]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  Andries Petrus Engelbrecht,et al.  Analysis of hyper-heuristic performance in different dynamic environments , 2014, 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE).

[14]  Xiaodong Li,et al.  Particle Swarms for Dynamic Optimization Problems , 2008, Swarm Intelligence.

[15]  Julien Georges Omer Louis Duhain.  Particle swarm optimisation in dynamically changing environments - an empirical study , 2012 .

[16]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[17]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

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

[19]  A. P. Engelbrecht.  Roaming Behavior of Unconstrained Particles , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

[20]  Raymond Chiong,et al.  Dynamic Function Optimization: The Moving Peaks Benchmark , 2013, Metaheuristics for Dynamic Optimization.

[21]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[22]  Andries Petrus Engelbrecht,et al.  Towards a more complete classification system for dynamically changing environments , 2012, 2012 IEEE Congress on Evolutionary Computation.

[23]  Tim Blackwell,et al.  Particle Swarm Optimization in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.