Abstract
Recent research on multi- and many-objective optimization has led to the development of various state-of-the-art algorithms which produce satisfactory results for various kinds of problems. However, in real life, the underlying objective functions or the characteristic landscape formed by the objectives may not be known beforehand. This makes it difficult for a user to choose the correct optimization algorithm. This paper proposes new indicators which attempt to summarize the population dynamics across iterations. The statistics of the population movement can help in identifying various features of the problem at hand and the capacity of an algorithm to deal with the challenges corresponding to the features. The analysis of population movement can enable further modifications of an existing algorithm according to the optimization problem. The indicators can also help in the development of adaptive optimization algorithms by providing feedback during the search for optimality.
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Acknowledgements
This work is funded by the project (DST-INRIA/2015-02/BIDEE/0978) of the Indo-French Centre for the Promotion of Advanced Research (IFCPAR).
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Sengupta, R., Pal, M., Saha, S., Bandyopadhyay, S. (2019). Population Dynamics Indicators for Evolutionary Many-Objective Optimization. In: Panigrahi, C., Pujari, A., Misra, S., Pati, B., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-13-0224-4_24
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DOI: https://doi.org/10.1007/978-981-13-0224-4_24
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