Exploitation Strategies in Conditional Markov Chain Search: A case study on the three-index assignment problem
This work addresses an incremental improvement in metaheuristic design for combinatorial optimization, specifically benefiting researchers and practitioners in optimization algorithms.
The study tackled the exploitation weakness of Conditional Markov Chain Search (CMCS) by proposing extensions, such as a two-stage CMCS, and applied it to the three-index assignment problem, showing that the two-stage version outperforms the single-stage one.
The Conditional Markov Chain Search (CMCS) is a framework for automated design of metaheuristics for discrete combinatorial optimisation problems. Given a set of algorithmic components such as hill climbers and mutations, CMCS decides in which order to apply those components. The decisions are dictated by the CMCS configuration that can be learnt offline. CMCS does not have an acceptance criterion; any moves are accepted by the framework. As a result, it is particularly good in exploration but is not as good at exploitation. In this study, we explore several extensions of the framework to improve its exploitation abilities. To perform a computational study, we applied the framework to the three-index assignment problem. The results of our experiments showed that a two-stage CMCS is indeed superior to a single-stage CMCS.