GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization

• URL: http://arxiv.org/abs/2312.07083v1
• Date: Tue, 12 Dec 2023 09:04:34 GMT
• Title: GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization
• Authors: Danial Yazdani (1), Mohammad Nabi Omidvar (2), Delaram Yazdani (3), Kalyanmoy Deb (4), and Amir H. Gandomi (1,5) ((1) Faculty of Engineering & Information Technology, University of Technology Sydney, (2) School of Computing, University of Leeds, and Leeds University Business School, (3) Liverpool Logistics, Offshore and Marine (LOOM) Research Institute, Faculty of Engineering and Technology, School of Engineering, Liverpool John Moores University, (4) BEACON Center, Michigan State University, (5) University Research and Innovation Center (EKIK), Obuda University)
• Abstract summary: It is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for single-objective, box-constrained, continuous numerical optimization.
• Score: 5.635586285644365
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives, such as the systematic evaluation of algorithms under controllable conditions. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for single-objective, box-constrained, continuous numerical optimization. Unlike existing approaches that rely on multiple baseline functions and transformations, GNBG utilizes a single, parametric, and configurable baseline function. This design allows for control over various problem characteristics. Researchers using GNBG can generate instances that cover a broad array of morphological features, from unimodal to highly multimodal functions, various local optima patterns, and symmetric to highly asymmetric structures. The generated problems can also vary in separability, variable interaction structures, dimensionality, conditioning, and basin shapes. These customizable features enable the systematic evaluation and comparison of optimization algorithms, allowing researchers to probe their strengths and weaknesses under diverse and controllable conditions.

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