Genetic algorithm and particle swarm algorithm parallel fusion evolution algorithm

Abstract

The invention relates to an evolutionary algorithm for parallel fusion of a genetic algorithm and a particle swarm optimization algorithm. The algorithm comprises the following steps: S1, randomly generating an initial population; S2, using a fitness function to calculate the fitness of the initial population; S3, implementing evolutionary calculation step, setting the minimum threshold of fitness function value as the termination condition of evolution calculation; S4, performing genetic operation to generate offspring population 1, and performing particle swarm evolution operation at the same time to generate offspring population 2; S5, combining offspring population 1 and offspring population The generation population 2 is merged and sorted according to the fitness, and the individuals with high fitness are combined as the offspring population 3; S6, the offspring population 3 continues to return to step S2 for loop operation until the termination condition of the evolution calculation is reached, and the output has the optimal fitness individual. The present invention solves the problems of low efficiency of the genetic algorithm in the later stage and the particle swarm algorithm is easy to fall into a local optimal solution, and improves the optimization efficiency and optimization effect of the evolutionary algorithm.

Description

technical field [0001] The invention relates to the field of evolutionary algorithms, in particular to an evolutionary algorithm in which a genetic algorithm and a particle swarm algorithm are fused in parallel. Background technique [0002] In recent years, with the rapid development of computer technology, in order to solve complex problems such as large space, nonlinearity, global optimization, and combinatorial optimization to a certain extent, many evolutionary algorithms have emerged, such as genetic algorithm and particle swarm optimization. Genetic algorithm is widely used, practical and robust, but its efficiency is low in the later stage. Particle swarm optimization algorithm has fast search speed, high efficiency and simple algorithm, but it is easy to fall into local optimal solution. [0003] Because of the unique advantages of these algorithms, they have received extensive attention from scholars at home and abroad, and set off a research boom. , task allocat...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: the genetic algorithm and the particle swarm evolution algorithm in the prior art have their own shortcomings (such as the low efficiency of the genetic algorithm in the later stage, and the particle swarm algorithm is easy to fall into the local optimal solution)

[0005] In order to solve the above technical problems, the present invention provides an evolutionary algorithm in which the genetic algorithm and the particle swarm algorithm are fused in parallel, which solves the problems that the genetic algorithm is inefficient in the later stage and the particle swarm algorithm is easy to fall into a local optimal solution, and improves the optimization of the evolutionary algorithm. Efficiency and optimization effect, the algorithm includes the following steps:

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