Abstract
This paper proposes a flexible software architecture for interactive multi-objective optimization, with a user interface for visualizing the results and facilitating the solution analysis and decision making process.
The architecture is modular, it allows for problem-specific extensions, and it is applicable as a post-processing tool for all optimization schemes with a number of different potential solutions. When the architecture is tightly coupled to a specific problem-solving or optimization method, effective interactive schemes where the final decision maker is in the loop can be developed.
An application to Reactive Search Optimization is presented. Visualization and optimization are connected through user interaction: the user is in the loop and the system rapidly reacts to user inputs, like specifying a focus of analysis, or preferences for exploring and intensifying the search in interesting areas.
The novelty of the visualization approach consists of using recent online graph drawing techniques, with sampling and mental map preserving schemes, in the framework of stochastic local search optimization.
Anecdotal results to demonstrate the effectiveness of the approach are shown for some relevant optimization tasks.
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Brunato, M., Battiti, R. (2010). Grapheur: A Software Architecture for Reactive and Interactive Optimization. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_26
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DOI: https://doi.org/10.1007/978-3-642-13800-3_26
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