Design and Implementation of Enhanced Linearization Process Based on Evolutionary Algorithm

Abstract

Nowadays, the recent strategy of covariance matrix adaptation evolution strategy (CMA-ES) has become a standard evolutionary technique for multi-objective optimization (MOO). To attain the linearized process of constant temperature anemometer (CTA) the unsophisticated procedure is offered. In this paper, to have the linearized process of CTA the function is optimized with the optimal values of mean square error which is determined by the nonlinear ratio metric logarithmic objective strategy to develop the signal linearity of CTA. Here, the modified multi-objective covariance matrix adapted evolution strategy (MMO- CMAES) is modified with the matrix function and generates the process for convergence is reduced to optimum the system with reliable access. The proposed system is processed with the reduction of the generation process which is used to adapt the matrix requirement and the efficient process of parallel enactment is done for temperature linearization. The proposed system is simulated and attained the results analysis by using Lab VIEW 7.1. The study's purpose is to have an efficient optimal resistor value, time complexity, reduction of noise and generations and reliable process.