A hybrid adaptive SAGA based on mutative scale chaos optimization strategy (CASAGA) is proposed to solve the slow convergence, incident getting into local optimum characteristics of the Standard Genetic Algorithm (SGA). The algorithm combined the parallel searching structure of Genetic Algorithm (GA) with the probabilistic jumping property of Simulated Annealing (SA), also used adaptive crossover and mutation operators. The mutative scale Chaos optimization strategy was used to accelerate the optimum seeking. Compared with SGA and MSCGA on some complex function optimization and several TSP combination optimization problems, the CASAGA improved the global convergence ability and enhanced the capability of breaking away from local optimal solution. PDFFulltextXMLReferencesCitation
How to cite this article
Haichang Gao, Boqin Feng, Yun Hou, Bin Guo and Li Zhu, 2006. Adaptive SAGA Based on Mutative Scale Chaos Optimization Strategy. Information Technology Journal, 5: 524-528.