Abstract
In 2006, a novel Group Search Optimizer (GSO) inspired by animal behavioral ecology was proposed. On unconstrained optimization problems, GSO has shown its superior performance. In this paper, the performance of it in coping with constrained problems is investigated. Several experiments are performed on 13 well known and widely used benchmark problems. The obtained results are presented and compared with the best known solution obtained so far. The experimental results show that GSO can find the exact or close to global optimal solutions on most problems. GSO has an ability of solving constrained problem and is an alternative bio-inspired optimization algorithm.
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Shen, H., Zhu, Y., Zou, W., Zhu, Z. (2011). Group Search Optimizer Algorithm for Constrained Optimization. In: Yu, Y., Yu, Z., Zhao, J. (eds) Computer Science for Environmental Engineering and EcoInformatics. CSEEE 2011. Communications in Computer and Information Science, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22691-5_9
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DOI: https://doi.org/10.1007/978-3-642-22691-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22690-8
Online ISBN: 978-3-642-22691-5
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