Title :
A Hybrid Optimized Algorithm Based on Simplex Method and Genetic Algorithm
Author :
Ren, Ziwu ; San, Ye
Author_Institution :
Control & Simulation Centre, Harbin Inst. of Technol.
Abstract :
Based on the simplex method and real-code genetic algorithm, a hybrid computational algorithm has been presented in this paper. In this hybrid genetic algorithm some improved genetic mechanisms, for example non-linear ranking selection, improved crossover operation combining the differential computation with arithmetic crossover and non-uniform mutation operation, are also adopted to overcome the slow convergence and premature problem in the simple genetic algorithm. The experimental results show that the new algorithm not only improves the global optimization performance, but also quickens the convergence speed and obtains robust results with good quality, which indicates this new algorithm is a promising approach for solving global optimization problems
Keywords :
differential equations; genetic algorithms; arithmetic crossover; crossover operation; differential computation; hybrid computational algorithm; hybrid genetic algorithm; hybrid optimized algorithm; nonlinear ranking selection; nonuniform mutation operation; real-code genetic algorithm; simplex method; Arithmetic; Computational modeling; Convergence; Diversity reception; Genetic algorithms; Genetic mutations; Intelligent control; Optimization methods; Robustness; Simulated annealing; differential; genetic algorithm; hybrid algorithm; simplex method;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713029