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.
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Amaya, J.E., Cotta, C., Leiva, A.J.F. (2010). A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_45
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DOI: https://doi.org/10.1007/978-3-642-15844-5_45
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