Adaptive cooperation in parallel memetic algorithms for rich vehicle routing problems

Designing and implementing cooperation schemes for parallel algorithms has become a very important task recently. The scheme, which defines the cooperation topology, frequency and strategies for handling transferred solutions, has a tremendous influence on the algorithm search capabilities, and can help balance the exploration and exploitation of the vast solution space. In this paper, we present both static and dynamic schemes - the former are selected before the algorithm execution, whereas the latter are dynamically updated on the fly to better respond to the optimisation progress. To understand the impact of such cooperation approaches, we applied them in the parallel memetic algorithms for solving rich routing problems, and performed an extensive experimental study using well-known benchmark sets. This experimental analysis is backed with the appropriate statistical tests to verify the importance of the retrieved results.