The Research Of Self-evolutionary Genetic Algorithm Based On Operator Optimization For Space Coordinate Systems Correction

Genetic algorithm is an intelligent algorithm based on genetic evolution in natural world,it has developed rapidly in recent years.Many improved genetic algorithms has been proposed,such as simulated annealing algorithm,niche genetic algorithm,coevolutionary genetic algorithm,multi-objective genetic algorithm,adaptive genetic algorithm and so on.Self-evolutionary genetic algorithm is an improved genetic algorithm which is proposed in "evolution algorithm optimization on vector field measurement system",it encodes crossover probability and mutation probability as two genes on chromosomes---"crossover gene" and "mutation gene".The two genes evolve with the individuals and self-adjust to fit the algorithm optimization process.In other words,the two parameters do not need to be set manually,they do not rely on the experience of engineers.Triaxial measurement techniques has a wide range of applications in real life,such as navigation,flight simulation platform research,radar range,fiber optic gyroscopes,vector hydrophone et.However,due to the processing technology,the installation process and human factors,the three-axis measurement system is not strictly orthogonal,and existing other errors.The errors can be divided into three types: ortho-errors between the axes in three-axis measuring devices;the scale factor error cause by the incomplete symmetrical electrical characteristics in three sensors;bias error cause by the sensors output drift when three-axis magnetometer is used.How to correct the errors of three-axis measuring system has become a hotspot for research.In the calibration process,if the three errors are measured directly,it needs expensive calibration equipment and the measurement process is more complicated.Therefore,an important way to calibrate three-axis measuring system is use correction algorithm.In this paper,self-evolutionary genetic algorithm is improved by adjusting the fine-tuning operators and introducing the population uniform initialization.At the same time,test functions were utilized to test the optimization performance of improved algorithm,the results show that the optimizing performance of new algorithm is improved.Then,use improved self-evolutionary genetic algorithm to calibrate the errors in three-axis measuring device Firstly,establish model,and determine the mapping relationship between the actual measurement space and ideal measurement space.Secondly,use improved self-evolutionary genetic algorithm to optimize this error correction problem,including the choice of encoding mechanism,the setting of the fitness function and design of genetic manipulations.Finally,correct the errors in three-axis measuring system.Use C language to simulate the questions mentioned above,the experimental results show that the improved self-evolutionary genetic algorithm has higher optimization accuracy and easier avoiding local optimum,this improved method for self-evolutionary genetic algorithm is feasible and effective.