Title :
Research of Multi-objective Optimization Based on Hybrid Genetic Algorithm
Author :
Jiang, Hua ; Xu, Guilin ; Deng, Zhenrong
Author_Institution :
Comput. & control Coll., Guilin uniersity of Electron. Technol., Guilin, China
Abstract :
In the process of solving multi-objective Pareto solution, the search ability in total area and the convergence characteristics can be reinforced by self-adjusting of aberrance probability in offspring evolution. Comparing with the typical hybrid genetic algorithm, the more effective optimization convergence can be obtained by using the improved hybrid genetic algorithm in solution for optimization problem. Numerical simulation based on some typical examples demonstrate the effectiveness of the proposed method.
Keywords :
Pareto optimisation; convergence of numerical methods; genetic algorithms; probability; Pareto solution; aberrance probability; convergence characteristics; hybrid genetic algorithm; multi-objective optimization; numerical simulation; offspring evolution; Arithmetic; Convergence; Design methodology; Educational institutions; Evolution (biology); Genetic algorithms; Numerical simulation; Optimization methods; Pareto optimization; Robustness; Hybrid genetic algorithm; Pareto solution; multi-objective optimization;
Conference_Titel :
INC, IMS and IDC, 2009. NCM '09. Fifth International Joint Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-5209-5
Electronic_ISBN :
978-0-7695-3769-6
DOI :
10.1109/NCM.2009.136
