Parameter optimization method based on improved genetic algorithm, computer equipment and storage medium

Abstract

The invention discloses a parameter optimization method based on an improved genetic algorithm, computer equipment and a storage medium, and belongs to the field of parameter optimization. The optimization method comprises the following steps: 1) defining an initial chromosome population; 2) constructing a dynamic evaluation index function of the electric vehicle control system, and optimizing toobtain chromosome fitness; 3) selecting by using a brocade selection algorithm to serve as a parent population; performing operation by using an adaptive crossover and mutation algorithm to generate afilial generation population; adjusting the optimization region of the nth chromosome in the jth step by adopting a self-adaptive search strategy, checking whether j reaches the maximum allowable optimization step number or not after the search is completed, and if not, returning to the step 2); and 4) finding out the chromosome individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the chromosome is the parameter value of the electric vehicle control system, and the invention solves the problems of complex modeling and large calculation amount in the parameter optimization process of the electric vehicle control system.

Description

technical field [0001] The invention belongs to the field of parameter optimization of an automobile control system, in particular to a parameter optimization method based on an improved genetic algorithm, computer equipment and a storage medium. Background technique [0002] The electric vehicle control system is a kind of multi-input multi-output nonlinear system with complex dynamic coupling characteristics. This system can be used to ensure the stability and maneuverability of the vehicle, improve the ability to complete tasks and the ride comfort of the vehicle, and enhance the safety of the vehicle. And reduce the driver's burden. The design of the control parameters of the electric vehicle control system has become the most direct and important key link to ensure the driving safety of electric vehicles. In the process of actually designing electric vehicle control systems, many engineers have to face the problem of parameter design and adjustment of complex systems w...

AI Technical Summary

Problems solved by technology

However, in the current process of using genetic algorithm to optimize the parameters of the electric vehicle control system, the algorithm may fall into a local optimum due to insufficient search of the solution space of the target problem, resulting in the failure to obtain the global optimal solution. The excessively large search range leads to the problem of slow convergence speed in the later stage. These problems have affected to a certain extent the effect of applying the genetic algorithm to the parameter optimization of the electric vehicle control system, which in turn affects the performance of the system.

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