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

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