Work

Bridging the Gap Between Theory and Practice: Fitness Landscape Analysis of Real-World Problems with Nearest-Better Network

Year: 2025

Type: article

Abstract: For a long time, there has been a gap between theoretical optimization research and real-world applications. A key challenge is that many real-world problems are black-box problems, making it difficult to identify their characteristics and, consequently, select the most effective algorithms to solve them. Fortunately, the Nearest-Better Network has emerged as an effective tool for analyzing the characteristics of problems, regardless of dimensionality. In this paper, we conduct an in-depth experimental analysis of real-world functions from the CEC 2022 and CEC 2011 competitions using the NBN. Our experiments reveal that real-world problems often exhibit characteristics such as unclear global structure, multiple attraction basins, vast neutral regions around the global optimum, and high levels of ill conditioning.

Source: Information

DOI: 10.3390/info16030190

Authors: Yiya Diao, Changhe Li, Junchen Wang, Sanyou Zeng, Shengxiang Yang

Institutions: China University of Geosciences, Anhui University of Science and Technology, De Montfort University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: National Natural Science Foundation of China