Abstract
We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.
This work was conducted in the frame of the research program at the Austrian Center of Competence in Mechatronics (ACCM), a part of the COMET K2 program of the Austrian government. The work-related projects are kindly supported by the Austrian government, the Upper Austrian government and the Johannes Kepler University Linz. The authors thank all involved partners for their support. This publication reflects only the authors’ views.
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Zăvoianu, AC., Lughofer, E., Amrhein, W., Klement, E.P. (2013). Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53856-8_32
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