Optimization heuristic for solving large-scale combinatorial problems.
The combinatorial optimization market is driven by the need to efficiently solve complex decision-making problems in industries including supply chain management, finance, machine learning, and advanced computing. Recent developments in artificial intelligence are accompanied by increasingly large computational problems. Traditional methods often struggle with NP-hard combinatorial optimization problems at scale. There is a growing demand for efficient, scalable algorithms as computational problems increase in complexity.
The inventor has developed a massively parallelized heuristic that can be applied to large-scale combinatorial optimization problems. The method uses probabilistic selection to help in exploring a wider range of potential globally optimal solutions, making it more likely to find the best configuration by avoiding getting stuck in local optima. This can be implemented in industries where complexity and data size create computational challenges. This method can handle NP-problems efficiently making it highly relevant across sectors including AI, finance, logistics, and quantum inspired computing.