A fuzzy hierarchical operator in the grey wolf optimizer algorithm

doi:10.1016/j.asoc.2017.03.048

A fuzzy hierarchical operator in the grey wolf optimizer algorithm

2017-08-01

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DOI：10.1016/j.asoc.2017.03.048

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The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf. This proposed operator is applied to the simulation of the hunting process in the algorithm and has 5 variants that are explained in more detail in this paper (centroid, weighted, based on the fitness and two variants using fuzzy logic). Notably the variants having the greatest impact in the GWO performance are based on the use of fuzzy logic. We also present the motivation and results of experiments, as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a comparison among all methods for 30, 64 and 128 dimensions and we conclude that the performance of the Hierarchical GWO algorithm is better when using a fuzzy variant of the hierarchical operator. (C) 2017 Elsevier B.V. All rights reserved.

Keywords:

Dynamic adaptation

Fuzzy logic

Performance

GWO

Benchmark functions

New operator

Hierarchical

Pyramid

A fuzzy hierarchical operator in the grey wolf optimizer algorithm

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