Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path

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

Institutions

Publication Details

Journal
IEEE Transactions on Evolutionary Computation
Published
2025-03-18
DOI
https://doi.org/10.1109/tevc.2025.3552685
Citations
1
Primary Topic
Regional Development and Policy
Type
article
Field-Weighted Citation Impact
8.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path

Ahmer Khan, Kalyanmoy Deb
1 citations
IEEE Transactions on Evolutionary Computation
Regional Development and Policy
8.52
article

Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path

Ahmer Khan, Kalyanmoy Deb
article en
1 citations

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.

IEEE Transactions on Evolutionary ComputationVol. 30(1)
Michigan State University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Regional Development and Policy
8.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.