The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization

Abstract A new stochastic method named hybrid generalized extremal optimization (HGEO) is proposed in this paper. It combines genetic algorithms (GAs) and generalized extremal optimization (GEO). In order to extend GEO’s mutation operator to accelerate convergence speed and be easily incorporated into HGEO, the real coded GEO is first developed to population-base GEO (PGEO), and then incorporated into the HGEO in the paper. Constraints consideration for using the HGEO and the effects of related operators are also investigated. Finally, the performance of the HGEO is fully investigated compared with other related algorithms to find the optimal power consumption for the semi-track air-cushion vehicle (STACV). The results show that the HGEO has better performance than GAs or other related simpler algorithms.

[1]  P. Bak,et al.  Self-organized criticality. , 1988, Physical review. A, General physics.

[2]  Aleksandra B. Djurišić,et al.  Elite genetic algorithms with adaptive mutations for solving continuous optimization problems: application to modeling of the optical constants of solids , 1998 .

[3]  Tim Hendtlass,et al.  Solving Problems with Hidden Dynamics – Comparison of Extremal Optimisation and Ant Colony System , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Min-Rong Chen,et al.  Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization , 2006, 2006 International Conference on Computational Intelligence and Security.

[5]  Kusum Deep,et al.  A new crossover operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

[6]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  Xin Wang,et al.  Study on automatic control system of a semi-tracked air-cushion vehicle , 1999, Proceedings of the IEEE International Vehicle Electronics Conference (IVEC'99) (Cat. No.99EX257).

[8]  Fabiano Luis de Sousa,et al.  Generalized extremal optimization: An application in heat pipe design , 2004 .

[9]  Zhe Luo,et al.  DESIGN OF A NOVEL SEMI-TRACKED AIR-CUSHION VEHICLE FOR SOFT TERRAIN , 2003 .

[10]  Kay Hamacher,et al.  Adaptive extremal optimization by detrended fluctuation analysis , 2007, J. Comput. Phys..

[11]  Mohamed El Bachir Menai,et al.  Efficient Initial Solution to Extremal Optimization Algorithm for Weighted MAXSAT Problem , 2003, IEA/AIE.

[12]  John M. Wilson,et al.  Comparing efficiencies of genetic crossover operators for one machine total weighted tardiness problem , 2008, Appl. Math. Comput..

[13]  Stefan Boettcher,et al.  Extremal optimization at the phase transition of the three-coloring problem. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[15]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[16]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[17]  A. Percus,et al.  Nature's Way of Optimizing , 1999, Artif. Intell..

[18]  Wei-Der Chang,et al.  A multi-crossover genetic approach to multivariable PID controllers tuning , 2007, Expert Syst. Appl..

[19]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[20]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[21]  S. Sitharama Iyengar,et al.  Automatic Correlation and Calibration of Noisy Sensor Readings Using Elite Genetic Algorithms , 1996, Artif. Intell..

[22]  Carlos A. Coello Coello,et al.  A simple multimembered evolution strategy to solve constrained optimization problems , 2005, IEEE Transactions on Evolutionary Computation.

[23]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[24]  N. R. Siriwardene,et al.  Selection of genetic algorithm operators for urban drainage model parameter optimisation , 2006, Math. Comput. Model..

[25]  Andrew Lewis,et al.  An Extended Extremal Optimisation Model for Parallel Architectures , 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

[26]  Min-Rong Chen,et al.  A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization , 2008, Eur. J. Oper. Res..

[27]  Stefan Boettcher,et al.  Extremal Optimization for the Sherrington-Kirkpatrick Spin Glass , 2004 .

[28]  Stefan Boettcher.  Extremal Optimization: Heuristics Via Co-Evolutionary Avalanches , 2000, Comput. Sci. Eng..

[29]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[30]  Sebti Foufou,et al.  A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics , 2006, Pattern Recognit. Lett..

[31]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[32]  S. Boettcher.  Extremal optimization for Sherrington-Kirkpatrick spin glasses , 2004, cond-mat/0407130.

[33]  Stefan Boettcher,et al.  Optimization with Extremal Dynamics , 2000, Complex..

[34]  Zhe Luo,et al.  Load distribution control system design for a semi-track air-cushion vehicle , 2007 .

[35]  Tao Zhou,et al.  Continuous extremal optimization for Lennard-Jones clusters. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.