Abstract
In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance strategy based on global crowding, which is addressed for pruning non-dominated solutions as well as preserving a wide-spread distributed solution set and maintaining population diversity. Later on, inspired by the conception of entropy in information theory, the entropy metrics is defined and applied to assess the proposed strategy. Two-dimensional and multi-dimensional numerical experiment results demonstrate that the proposed strategy shows better performance in the entropy reduction and losses of uniform distribution than traditional diversity maintenance strategies.
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Chen, Q., Xiong, S., Liu, H. (2009). Diversity Maintenance Strategy Based on Global Crowding. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_2
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DOI: https://doi.org/10.1007/978-3-642-01513-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
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