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Dynamic Optimization Through Continuous Interacting Ant Colony

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3172))

Abstract

In recent past, optimization of dynamic problems has evoked the interest of the researchers in various fields which has resulted in development of several increasingly powerful algorithms. Unlike in static optimization, where the final goal is to find the fixed global optimum, in dynamic optimization the aim is to find and follow the evolution of the global optimum during the entire optimization time.

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References

  1. Branke, J.: Evolutionary Optimization in Dynamic Environments. Genetic Algorithms and Evolutionary Computation, vol. 3. Kluwer, Dordrecht (2001)

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  2. Dréo, J., Siarry, P.: Continuous Interacting Ant Colony Algorithm Based on Dense Heterarchy. Future Generation Computer Systems (2004) (to appear)

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  3. Xiong, Q., Jutan, A.: Continuous optimization using a dynamic simplex method. Chemical Engineering Science 58(16), 3817–3828 (2003)

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© 2004 Springer-Verlag Berlin Heidelberg

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Dréo, J., Siarry, P. (2004). Dynamic Optimization Through Continuous Interacting Ant Colony. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2004. Lecture Notes in Computer Science, vol 3172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_46

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  • DOI: https://doi.org/10.1007/978-3-540-28646-2_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22672-7

  • Online ISBN: 978-3-540-28646-2

  • eBook Packages: Springer Book Archive

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