Document Type : Original Research Paper

Authors

• M. Amoozegar ¹
• S. Golestani ²

¹ Department of Computer and Information Technology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.

² PhD Candidate, Computer Science, University of Saskatchewan, Saskatoon, Canada.

Abstract

Background and Objectives: In recent years, various metaheuristic algorithms have become increasingly popular due to their effectiveness in solving complex optimization problems across diverse domains. These algorithms are now being utilized for an ever-expanding number of real-world applications across many fields. However, there are two critical factors that can significantly impact the performance and optimization capability of metaheuristic algorithms. First, comprehensively understanding the intrinsic behavior of the algorithms can provide key insights to improve their efficiency. Second, proper calibration and tuning of an algorithm's parameters can dramatically enhance its optimization effectiveness.
Methods: In this study, we propose a novel response surface methodology-based approach to thoroughly analyze and elucidate the behavioral dynamics of optimization algorithms. This technique constructs an informative empirical model to determine the relative importance and interaction effects of an algorithm's parameters. Although applied to investigate the Gravitational Search Algorithm, this systematic methodology can serve as a generally applicable strategy to gain quantitative and visual insights into the functionality of any metaheuristic algorithm.
Results: Extensive evaluation using 23 complex benchmark test functions exhibited that the proposed technique can successfully identify ideal parameter values and their comparative significance and interdependencies, enabling superior comprehension of an algorithm's mechanics.

Conclusion: The presented modeling and analysis framework leverages multifaceted statistical and visualization tools to uncover the inner workings of algorithm behavior for more targeted calibration, thereby enhancing the optimization performance. It provides an impactful approach to elucidate how parameter settings shape algorithm searche so they can be calibrated for optimal efficiency.

Keywords

• Parameter Analysis
• Interaction Effect
• Fine-tuning
• Response Surface Model (RSM)
• Gravitational Search Algorithm (GSA)

Main Subjects

• Swarm intelligence

Open Access

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Publisher

Shahid Rajaee Teacher Training University