Design and Implementation of Improved Multi-objective Genetic Algorithm Based on Uniform Distribution

In production and daily life, with the development and progress of various technologies, many practical problems gradually transform from the form of a single goal to the form of multiple goals. In single objective optimization problems, only finding the optimal solution that satisfies the conditions for a single objective is considered, while multi-objective optimization problems are looking for the optimal solution set that satisfies multiple objectives simultaneously under given conditions When solving multi-objective optimization problems, the NSGA II algorithm is generally used When using NSGA II algorithm to solve multi-objective optimization problems, there are some problems such as slow Rate of convergence, weak stability, and easy to fall into local optimization. An improved algorithm was proposed to address this issue. Introducing interval uniform distribution module during population initialization, dynamically adjusting crossover and mutation probabilities during the algorithm process. The improved multi-objective genetic algorithm based on uniform distribution was applied to classic examples and compared with the application results of NSGA II algorithm and particle swarm optimization algorithm. The experimental results show that the improved multi-objective genetic algorithm performs best in terms of uniformity and convergence, followed by the NSGA II algorithm, and the particle swarm algorithm has the worst results in comparative experiments.