A multiobjective evolutionary algorithm for optimizing the small-world property.

ABSTRACT: Small-world effect plays an important role in the field of network science, and optimizing the small-world property has been a focus, which has many applications in computational social science. In the present study, we model the problem of optimizing small-world property as a multiobjective optimization, where the average clustering coefficient and average path length are optimized separately and simultaneously. A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. Experimental results have proved that the presented method is capable of solving this problem efficiently, where a uniform distribution of solutions on the Pareto-optional front can be generated. The optimization results are further discussed t

SUBMITTER: Zhang R 

PROVIDER: S-EPMC11614276 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature