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Differential evolution algorithm-based modularity optimization method

A differential evolution algorithm and optimization method technology, applied in the field of optimization, can solve problems such as ignoring convergence ability, poor quality of optimal community division, topological information destroying global optimal community division search space, etc., to reduce blindness and strengthen exploration ability , the effect of strengthening the mining capacity

Inactive Publication Date: 2018-06-15
DALIAN NATIONALITIES UNIVERSITY
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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.

Method used

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  • Differential evolution algorithm-based modularity optimization method
  • Differential evolution algorithm-based modularity optimization method
  • Differential evolution algorithm-based modularity optimization method

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Embodiment 1

[0061] This embodiment provides a modularity optimization algorithm based on DE, specifically including:

[0062] 1. Modularity optimization algorithm based on DE:

[0063] (1) CDEMO algorithm, the algorithm flow chart is as follows figure 2 Shown:

[0064] 1: population initialization;

[0065] 1.1 Set network parameters, including node number n, adjacency matrix adj, community correction threshold δ. Set the parameters of the DE algorithm, including the individual dimension D, the population size NP, the number of population iterations t and the maximum number of iterations t max ;

[0066] 1.2 Randomly initialize the population pop with the individual representation of the community label;

[0067] 2: Identify and record the optimal solution

[0068] 2.1 Identify and record the optimal individual X in the t generation population pop gbest,t ;

[0069] 2.2 Identify and record each individual X in the t generation population pop i,t The historical optimal solution X...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08
CPCG06N3/086
Inventor 毕学良肖婧任宏菲许小可
Owner DALIAN NATIONALITIES UNIVERSITY
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