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3.3 Test Conclusions
From the test results, some conclusions can be obtained. First of all, for some test functions (e.g. F 2 , F 3
and some cases of F 6 , Fy), three algorithms are equivalent. The reason is that genetic algorithm has
strong searching ability for some optimization problems, and can find the solution directly Secondly,
hybrid algorithm has the best searching ability among three algorithms. It can find solutions in all test
functions. Furthermore, the hybrid algorithm has the highest searching efficiency which requires the
least amount of calculations.
4. CONCLUDING REMARKS
Generally, an efficient global optimization algorithm should has local searching ability, and can
transfer from a local optimization status to another. When genetic algorithm and simplex are combined
together, simplex not only can enforce the local searching ability, but also provided new individuals for
GA. The test results showed that the hybrid algorithm is an efficient optimization method.
References
1. PAN Zhengjun, KANG Lishan, CHEN Yuping. (1998). Evolutionary Computation, Tsmghua, China.
2. HAN Wei, LIAO Zhenpeng. (1999). A Note on the Convergence of Genetic Algorithms. Journal of
Earthquake Engineering and Engineering Vibration 19:4, 13-16.
3. CUI Jianwen. (1998). The Global Optimization Method for Inverting Velocity Profile with Rayleigh
Surface Wave Data, Institute of Engineering Mechanics, China
4. Scrrwefel H.P. (1981), Numerical Optimization of Computer Models. John Wiley, UK.