LIONsolver originates from research principles in Reactive Search Optimization[2] advocating the use of self-tuning schemes acting while a software system is running.
Version 2.0 of the software was released on Oct 1, 2011, covering also the Unix and Mac OS X operating systems in addition to Windows.
The modeling components include neural networks, polynomials, locally weighted Bayesian regression, k-means clustering, and self-organizing maps.
The software architecture of LIONsolver[5] permits interactive multi-objective optimization, with a user interface for visualizing the results and facilitating the solution analysis and decision-making process.
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.