An Improved Time-Sensitive Metaheuristic Framework for Combinatorial Optimization

We introduce a metaheuristic framework for combinatorial optimization. Our framework is similar to others (e.g. [1]) in that it is modular enough that important components can be independently developed. Ours is different in several aspects. It supports several built-in components such as combinatorial representations and search heuristics to facilitate the creation of a new optimizer for a wide range of combinatorial problems. The inclusion of different types of metaheuristics allows us to compose them and create a hybrid search that is on average better than each individual metaheuristic. Additionally, the system guarantees the feasibility of returned solutions for combinatorial problems that permit infeasible solutions. We, further, propose a generic method to optimize bottle-neck problems efficiently under the local-search framework.