Differential evolution algorithm-based modularity optimization method

Abstract

The invention discloses a differential evolution algorithm-based modularity optimization method. The method comprises the steps of initializing a population; setting network parameters including a node number n, an adjacent matrix adj and a community correction threshold delta; setting DE algorithm parameters including an individual dimension D, a population size NP, a population iteration frequency t and a maximum iteration frequency tmax; randomly initializing the population pop in an individual representation mode of a community label; then identifying and recording an optimal solution; when the population iteration frequency is smaller than the maximum population iteration frequency, automatically adding 1 to the population iteration frequency, and when the condition is met, ending thecirculation of 3.1-3.5; performing community correction based on neighborhood information; and outputting Xgbest in the pop, wherein t serves as final optimal community division. The method improvesthe accuracy, stability and expandability of the optimal community division; and complex networks with very fuzzy community structures are involved.

Description

technical field [0001] The invention relates to an optimization method, in particular to a DE-based modularity optimization method. Background technique [0002] In recent years, stochastic optimization algorithms, especially evolutionary algorithms (Evolutionary Algorithms, EAs), have been successfully applied to modularity optimization problems, such as genetic algorithm (Genetic Algorithm, GA), particle swarm optimization algorithm (Particle Swarm Optimization, PSO), Memetic algorithm, ant colony Optimization algorithm, clonal selection and differential evolution algorithm (DifferentialEvolution, DE), etc. It is worth noting that the modularity optimization method based on EA shows significant advantages in various detection problems due to its powerful global optimization ability. In addition, considering that it is difficult to obtain prior information in real-world networks, this type of algorithm does not require any prior information (such as the number of communiti...

AI Technical Summary

Problems solved by technology

However, as far as we know, in existing algorithms, basic EAs are usually directly used as optimization strategies and their convergence ability is ignored, which leads to premature convergence of EAs and poor quality of the optimal community division obtained.

At the same time, although some existing algorithms have improved the evolutionary operation in EAs to meet the needs of community detection by fusing network topology information, the inappropriate use of topology information destroys the search space for global optimal community division.

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