A memetic cooperative optimization schema and its application to the tool switching problem

Abstract

This paper describes a generic (meta-)cooperative optimization schema in which several agents endowed with an optimization technique (whose nature is not initially restricted) cooperate to solve an optimization problem. These agents can use a wide set of optimization techniques, including local search, population-based methods, and hybrids thereof, hence featuring multilevel hybridization. This optimization approach is here deployed on the Tool Switching Problem (ToSP), a hard combinatorial optimization problem in the area of flexible manufacturing. We have conducted an ample experimental analysis involving a comparison of a wide number of algorithms or a large number of instances. This analysis indicates that some meta-cooperative instances perform significantly better than the rest of the algorithms, including a memetic algorithm that was the previous incumbent for this problem. © 2010 Springer-Verlag.