Image-Based Benchmarking and Visualization for Large-Scale Global Optimization
Kyle Robert Harrison, Azam Asilian Bidgoli, Shahryar Rahnamayan,, Kalyanmoy Deb

TL;DR
This paper introduces an innovative image-based visualization framework for large-scale global optimization that preserves dimensions, enhances scalability, and enables real-time process visualization, improving understanding of complex search behaviors.
Contribution
The paper presents the first scalable, dimension-preserving visualization method that links decision variables directly to image pixels, facilitating better analysis of optimization processes.
Findings
Effective visualization of diverse optimization problems
Enhanced understanding of search dynamics in real-time
Compatibility with various mapping schemes
Abstract
In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer. Towards this goal, an image-based visualization framework, without dimension reduction, that visualizes the solutions to large-scale global optimization problems as images is proposed. In the proposed framework, the pixels visualize decision variables while the entire image represents the overall solution quality. This framework affords a number of benefits over existing visualization techniques including enhanced scalability (in terms of the number of decision variables), facilitation of standard image processing techniques, providing nearly infinite benchmark cases, and explicit alignment with human perception. Furthermore, image-based visualization can be used to visualize the…
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