A Framework for the Development of Local Search Heuristics for Combinatorial Optimization Problems

In the study of heuristics for combinatorial problems, it is often important to develop and compare, in a systematic way, different algorithms, strategies, and parameters for the same problem. The central issue in this paper is that, by modeling (using the object-oriented programming paradigm) in separate classes the different aspects involved in local search based methods, we increase our ability to construct and compare heuristics, independently of their implementations. Our main goal is to provide an architectural basis for the implementation and comparison of different local search heuristics. The proposed framework, described using design patterns, encapsulates, in abstract classes, different aspects involved in local search heuristics, such as algorithms for the construction of initial solutions, methods for neighborhood generation, and movement selection criteria. The classification of these aspects simplifies their implementations, and invites new extensions, in the form of subclasses. Code reuse, resulting from by the implementation of common aspects of a family of heuristics, allows a better platform for comparison, since a large part of the code is common to all members of the family.