Abstract
In this paper, the authors empirically investigate performance of the grey wolf optimizer (GWO). A test suite of six non-linear benchmark functions, well studied in the swarm and the evolutionary optimization literature, is selected to highlight the findings. The test suite contains three unimodal and three multimodal functions. The experimental results demonstrate the advantages and weaknesses of the GWO. In case of unimodal problems, initially it hastens towards the optimal solution but soon slows down because of the diversity problem. A similar behaviour is seen in case of multimodal problems with a difference that because of its behaviour it easily sticks to local optima, loses its diversity and stops any further progress. The reason is that it lacks information sharing in the pack. This insight led the authors to propose a modified GWO called the modified grey wolf optimizer (MGWO). An empirical study of the proposed algorithm MGWO shows its promising performance as the obtained results are superior to the GWO for all the test functions.
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Avadh Kishor, Singh, P.K. (2016). Empirical Study of Grey Wolf Optimizer. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_87
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DOI: https://doi.org/10.1007/978-981-10-0448-3_87
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