A region-based quantum evolutionary algorithm (RQEA) for global numerical optimization

This work presents the region-based quantum evolutionary algorithm (RQEA) for solving numerical optimization problems. In the proposed algorithm, the feasible solution space is decomposed into regions in terms of quantum representation. As the search progresses from one generation to the next, the quantum bits evolve gradually, increasing the probability of selecting regions that yield good fitness values. Through the inherent probabilistic mechanism, the RQEA initially behaves as a global search algorithm and gradually evolves into a local search algorithm, resulting in a good balance between exploration and exploitation. The RQEA is applied to a series of numerical optimization problems. The experiments show that the results obtained by the RQEA are better than those obtained using state-of-the-art QEA and DEahcSPX.

[1]  Weicai Zhong,et al.  A multiagent genetic algorithm for global numerical optimization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Jong-Hwan Kim,et al.  Quantum-Inspired Evolutionary Algorithms With a New Termination Criterion , H Gate , and Two-Phase Scheme , 2009 .

[3]  Zhijian Wu,et al.  Algorithm based on heuristic subspace searching strategy for solving investment portfolio optimization problems , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[4]  Bo Liu,et al.  An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Hitoshi Iba,et al.  Real-Coded Estimation of Distribution Algorithm , 2003 .

[6]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub /spl epsi// gate, and two-phase scheme , 2004, IEEE Transactions on Evolutionary Computation.

[7]  Ville Tirronen,et al.  An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production , 2008, Evolutionary Computation.

[8]  Jong-Hwan Kim,et al.  On the Analysis of the Quantum-inspired Evolutionary Algorithm with a Single Individual , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[10]  Kenneth A. De Jong,et al.  An Analysis of Local Selection Algorithms in a Spatially Structured Evolutionary Algorithm , 1997, ICGA.

[11]  Shuyuan Yang,et al.  A novel quantum evolutionary algorithm and its application , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[13]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[14]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.

[15]  Kumar Chellapilla,et al.  Local search operators in fast evolutionary programming , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[16]  Ville Tirronen,et al.  Scale factor local search in differential evolution , 2009, Memetic Comput..

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

[18]  Yong Lu,et al.  A robust stochastic genetic algorithm (StGA) for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.

[19]  Yun-Wei Shang,et al.  A Note on the Extended Rosenbrock Function , 2006, Evolutionary Computation.

[20]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[21]  Francisco Herrera,et al.  Memetic algorithm with Local search chaining for large scale continuous optimization problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[22]  Gexiang Zhang,et al.  A novel Memetic Algorithm based on real-observation Quantum-inspired evolutionary algorithms , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[23]  Yangyang Li,et al.  Quantum-Inspired Immune Clonal Algorithm for Global Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[25]  Tung-Kuan Liu,et al.  Hybrid Taguchi-genetic algorithm for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.

[26]  Grant Dick.  The spatially-dispersed genetic algorithm: an explicit spatial population structure for GAs , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[27]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[28]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..

[29]  Jean-Michel Renders,et al.  Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[30]  Yanchun Liang,et al.  A novel quantum swarm evolutionary algorithm and its applications , 2007, Neurocomputing.

[31]  Yu-Xuan Wang,et al.  Hybrid particle swarm optimizer with tabu strategy for global numerical optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[32]  Shuhei Kimura,et al.  High dimensional function optimization using a new genetic local search suitable for parallel computers , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[33]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[34]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[35]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[36]  Kwong-Sak Leung,et al.  Asynchronous self-adjustable island genetic algorithm for multi-objective optimization problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[37]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[38]  Yuehui Chen,et al.  A Region Reproduction Algorithm for global numerical optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[39]  Eduardo Huedo,et al.  A Grid-Oriented Genetic Algorithm , 2005, EGC.

[40]  Qingfu Zhang,et al.  DE/EDA: A new evolutionary algorithm for global optimization , 2005, Inf. Sci..

[41]  Hisao Ishibuchi,et al.  Performance evaluation of combined cellular genetic algorithms for function optimization problems , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[42]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[43]  Rui Zhang,et al.  Improved Quantum Evolutionary Algorithm for Combinatorial Optimization Problem , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[44]  Francisco Herrera,et al.  Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems , 2009, LION.

[45]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .