Abstract
Column generation is a basic tool for the solution of large-scale mathematical programming problems. We present a class of column generation algorithms in which the columns are generated by derivative free algorithms, like population-based algorithms. This class can be viewed as a framework to define hybridization of free derivative algorithms. This framework has been illustrated in this article using the Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms, combining them with the Nelder-Mead (NM) method. Finally a set of computational experiments has been carried out to illustrate the potential of this framework.
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Espinosa-Aranda, J.L., Garcia-Rodenas, R., Angulo, E. (2013). A Framework for Derivative Free Algorithm Hybridization. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_9
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DOI: https://doi.org/10.1007/978-3-642-37213-1_9
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