Dynamic Complex Network, Exploring Differential Evolution Algorithms from Another Perspective

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed these connections, known as complex networks, embody underlying principles governing system’s behavior. While have been previously applied in field evolutionary computation, prior studies limited ability reach conclusive conclusions. Based on our investigations, we are against notion that there is a direct link between network structure algorithm and its performance, demonstrate this experimentally. In paper, address limitations analyzing dynamic structures five algorithms across three different problems. By incorporating mathematical distributions utilized research, not only generate novel insights but also refine challenge previous Specifically, introduce biased Poisson distribution describe algorithm’s exploration capability power-law represent exploitation potential during convergence process. Our aim redirect research interplay computation towards structures, elucidating black-box optimization process via networks.