During the evolution process of differential evolution algorithm, good solutions arc generated and the
‘survival of the fittest’theory of Darwin is employed to select the better solutions, which results in failures of the
abandoned individual’s effective component and the reduction of population diversity.Thus the differential evolution
algorithm is not able to explore new space and traps in local optima. So the differential evolution algorithm has been
shown to have certain wcaknesses,especially if the global optimum should be located using a limited number of funr
tion evaluations, In order to remedy these defects of the differential evolution algorithm mentioned above, weighting
space exploration and exploitation is employed for improving it to enhance the convergence speed and solution quali-
ty.ln this paper,improved differential evolution algorithm based on Uaussian disturbance and immune search starte-
gy is proposed to solve the global optimization problems. Our approach combines several features of previous evolu-
tion algorithms in a unique manner.ln the novel approach,firstly,two parameters,scaling factor and crossover rate，
are self-adapted by immune system. Secondly, in the crossover phase the best vector is disturbed by Gaussian proba-
bility distribution.Thirdly,the trial vector is obtained by crossover between the mutation vector and the disturbed
best vector, In the optimizing process Gaussian disturbance increase the variety of the individual，which can make the
algorithm avoid trapping into the local optima and improve its performance. It is shown empirically that the novel im-
proved differential evolution algorithm has high performance in solving the benchmark functions.The Gaussian dis-
turbance is also employed for local optimiation to avoid population diveristy descent and individual stagnant evolu-
t lon.The results of experiments show that the gaussian disturbance in the crossover phase can improve the ability of
searching an optimum solution and increase the convergence speed.
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