Research On Optimization Of Power Generation Unit Combination Based On Intelligent Hybrid Algorithm

A multi-objective optimization problem is a problem that needs to optimize and solve two or more targets at the same time.While optimizing one target may cause degradation of other targets,the solution of this type of problem requires finding a group that can make each A solution for optimizing performance balance between goals.The unit combination optimization problem is a typical problem of multi-objective optimization problems.Since its research can bring significant economic benefits,various methods are proposed to solve this problem.Genetic algorithm has great advantages in solving the problem of unit combination optimization,but it also has the disadvantages of being easily trapped in local optimum and weak search ability.Particle swarm algorithm has the advantages of fast search speed and high efficiency,but it has not good effect of processing discrete problems.Aiming at these problems,this paper does research and improvement on this problem and applies it to the unit combination optimization problem.In this paper,the genetic algorithm and the particle swarm algorithm are improved and combined.By studying the operation process of the genetic algorithm and the particle swarm algorithm and their respective improved algor ithms,the advantages and disadvantages of the genetic algorithm and the particle swarm algorithm are analyzed in depth.And select the combination method.Aiming at the problem of weak local search ability of traditional genetic algorithm,this paper uses a method combining genetic algorithm and particle swarm algorithm to improve the local search ability of the algorithm and speed up the convergence speed.The hybrid intelligent algorithm and a single genetic algorithm and particle swarm optimization algorithm are used to optimize the test function at the same time.The experimental results are compared to verify the performance of the hybrid intelligent optimization algorithm.The hybrid intelligent optimization algorithm is applied to the unit combination optimization problem,and energy-saving and emission-reduction constraints are added to the objective function to fulfill the requirements of achieving multiple objective functions.The experimental results of genetic algorithm and particle swarm optimization algorithm are compared to further prove the good performance of hybrid intelligent optimization algorithm.