Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and network design. These problems involve finding the best possible solution from a finite set of discrete options by maximizing or minimizing an objective function subject to specified constraints. In such problems, the number of feasible solutions increases exponentially with the problem size, making it nearly impossible to find optimal solutions.
Researchers discover a smarter way to solve vehicle routing problems using adaptive swarm learning
