Research On The Method Of Solving The Initial Interior Point Of Constrained Optimization Problem Based On Real-coded Genetic Algorithm

The constraints optimization problem involves a wide range of fields,providing strong support for system optimization and management.The theory and algorithm of the constraint extreme value problem are derived from the optimization theory and method,the main research content is how to find the optimal scheme in many schemes.Optimization methods,such as interior point method and compound method are the most commonly used methods to solve this kind of problem.However,in the application of internal point method and composite method optimization algorithm,we must first find an initial point.For some problems,because of the lack of prior knowledge,it is difficult to give a feasible initial point,so the study of the initial point of the constrained optimization problem has significant theoretical significance and application value.Therefore,this paper makes a deep analysis and research in this field,and gives a new method based on real-coded genetic algorithm to solve the initial interior point of constraint optimization problem.Based on the comprehensive research of domestic and foreign literatures,use of mathematics,computer science and other multi-disciplinary comprehensive analysis,Based on the theory of constrained function method,a model based on improved real-coded genetic algorithm for solving the initial interior point problem of constrained optimization problem is proposed,And the example is calculated.The main findings are as follows:(1)In this paper,a method of solving the initial interior point of the constraint optimization problem is presented.The traditional method of solving needs to ensure that the constraint is have derivative,and the new method proposed in this paper does not have these requirements,so it is more universal.(2)The mathematical model of the initial interior point of the constrained optimization problem based on the improved real-coded genetic algorithm is proposed.(3)The construction method and model of the fitness function based on the real number genetic algorithm are given to solve the initial interior point of the constrained optimization problem.(4)The evolutionary strategy presented in this paper has the following characteristics: 1)The crossover probability is taken at 1 to ensure that all parent pairs need to be crossed,choosing such a crossover probability can increase the number of offspring individuals,can increase the possibility of generating more outstanding individuals,so that the algorithm can be improved speed of operation.2)The individual generated by the cross operation and the parent to retain the m elite individuals together to sort,re-select m elite individuals to retain,this avoids the fact that the excellent individuals produced by the crossover operation are destroyed during the mutation operation,so that the excellent individuals produced by the cross operation can survive.3)Sort the individuals generated by the mutation operation,then use the m individuals who were retained by the elite replaced the worst m individuals after the mutation operation,form a new population.This effectively ensures the diversity of the population,but also can make the optimal results obtained will not be worse than the previous results obtained.(5)Carried out example calculation,the results show that the method is an effective method.The method does not need to seek derivative of constraints,so more practical than the traditional method.