A Quick Optimizing Multi-variables Method with Complex Target Function Based on the Principle of Artificial Immunology.

Choice of ADPCM's step-size updating factors M has a sea capacity of computing that optimizes multi-variables with complex target function. There is the effective scheme such as GA or MEA but its convergence rate becomes too slowly in the neighborhood of peak value of multi-peak function to come away from local optimization. The Clone Mind Evolution Algorithm (CMEA) that introduces the clone operator to reserve the strong component of the weak individual to next iterativeness, which effect is very obvious with testing the typical function, comes into the MEA's similartaxis operator and is used to optimize ADPCM's 8 step-size updating factors. The experiment result shows that the CMEA's SNR has been reformed average 1.03dB every generation, which is exceeding MEA's by 0.4dB, in beginning five of iterativeness and overrun the MEA's from generation 5. Furthermore, the MEA's quantity of computing is equal to CMEA's by 1.67 times and the latter is of anti-prematurely. [ABSTRACT FROM AUTHOR]