Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path
Abstract
In practice, there is often a need to update the currently implemented (CI) solution to achieve better performance goals catering to new demands or adoption of new technologies. However, the new optimal solution, found by re-optimizing the problem, may be quite different from the CI solution implicating large costs, major changes, and laborious efforts, causing an apathy for its adoption. For such scenarios, we propose a concept of an “innovation path” (IP), containing a sequence of transitional solutions from the existing to the new target solution with gradual and controlled change from one to the next. To discover such intermediate solutions of the IP, we propose a bi-objective formulation with dynamic step-constraints as an IP Problem (IPP), such that a finite set of Pareto-optimal solutions of the resulting IPP become the desired intermediate IP solutions. Due to required gradual discovery of IP solutions, the IP-seeking task happens to be a non-trivial task. We demonstrate the working of the proposed approach on a number of single, two-objective, and many-objective test and engineering problems. The paper concludes with a number of extensions of this study, but the results of this study clearly indicate the usefulness of the proposed approach to other practical problems.
Authors: Ahmer Khan, Kalyanmoy Deb
Published in: IEEE Transactions on Evolutionary Computation (2025)