Dynamic Complex Network, Exploring Differential Evolution Algorithms from Another Perspective

Yifei Yang, Sichen Tao, Haichuan Yang, Zijing Yuan and Zheng Tang ()
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Yifei Yang: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Sichen Tao: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Haichuan Yang: Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan
Zijing Yuan: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Zheng Tang: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan

Mathematics, 2023, vol. 11, issue 13, 1-16

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

Keywords: complex system; dynamic complex network; evolutionary algorithms; differential evolution algorithms; population interaction network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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