Multilevel diversification and intensification in metaheuristics

Abstract

The performance of metaheuristics deteriorates very rapidly because the complexity of the problem usually increases with its size and the solution space of the problem increases exponentially with the problem size. Because of these two issues, optimization search techniques tend to spend most of the time exploring a restricted area of the search space preventing the search to visit more promising areas, and thus leading to solutions of poor quality. Designing efficient optimization search techniques requires a tactical interplay between diversification and intensification. The former refers to the ability to explore many different regions of the search space, whereas the latter refers to the ability to obtain high quality solutions within those regions. In this paper, three well known metaheuristics (Tabu Search, Memetic Algorithm and Walksat) are used with the multilevel context. The multilevel strategy involves looking at the search as a process evolving from a k-flip neighborhood to the standard I-flip neighborhood-based structure in order to achieve a tactical interplay between diversification and intensification. Benchmark results exhibit good prospects of multilevel metaheuristics.