Most people think optimization is about “making models smarter.”
In reality, it’s often about making search less dumb.
I recently worked on a hybrid metaheuristic for the Vehicle Routing Problem with Time Windows (VRPTW)—a classic logistics problem where exact methods quickly become impractical.
It can be viewed here: https://www.sciencedirect.com/org/science/article/pii/S1947828325000025
Instead of relying on a single strategy, I combined simple heuristics (like nearest-neighbor initialization) with evolutionary search (genetic algorithms + mutation strategies).
The result: better solutions without exploding complexity.
What stood out to me is this:
small structural changes in how you search the solution space can outperform much more “complex” modeling approaches.
Optimization is less about intelligence—and more about design of exploration.
I’ll be sharing more experiments and breakdowns as I build in AI systems and optimization.
If you’ve worked on optimization, routing problems, or hybrid metaheuristics, I’d love to hear your thoughts.
Feel free to drop questions or share what you’re working on in the comments—happy to discuss ideas, trade-offs, and approaches.