Dynamic multi‐objective optimisation of complex networks based on evolutionary computation

Abstract

AbstractAs the problems concerning the number of information to be optimised is increasing, the optimisation level is getting higher, the target information is more diversified, and the algorithms are becoming more complex; the traditional algorithms such as particle swarm and differential evolution are far from being able to deal with this situation effectively, and the multi‐objective evolutionary algorithm (MOEA) was born. Multi‐objective evolutionary algorithms help users to quickly obtain the data they want from the huge amount of complex network data, which greatly improves the efficiency. The multi objective evolutionary algorithm (MOEA) is simple, effective, and versatile, making it extremely attractive when solving multi‐objective optimisation problems. Since the distribution of the initial population affects the accuracy of the algorithm to some extent, this paper proposes to combine the mathematical calculus of evolutionary computation with the dynamics of complex networks as a way to carry out multi‐objective optimisation problems and find a more reasonable way for human information extraction. .The experimental results show that the set of non‐dominated solutions obtained by the algorithms designed is closer to the Pareto frontier, and the distribution of the searched non‐dominated solutions is more uniform.