Hybrid methods with decomposition for large scale optimization

Many real-world optimization problems are large scale, expensive to evaluate, and difficult to formulate, involving thousands of variables and constraints. This project aims to develop advanced approaches for solving large scale real-world optimization problems by taking an inter-disciplinary approach integrating ideas from mathematical programming and meta-heuristics. The project will make novel and significant contributions to improving state-of-the-art large scale optimization algorithms in terms of scalability, effectiveness, and efficiency for real-world problem solving. The outcomes of this project will bring about greater understanding of real-world large scale optimization, and deliver practical solutions to these problems.