Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior
He, S.
Wu, Q.H.
Saunders, J.R.
Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool, UK;
This paper appears in: Evolutionary Computation, IEEE Transactions on
Publication Date: Oct. 2009
Volume: 13,
Issue: 5
On page(s): 973-990
ISSN: 1089-778X
INSPEC Accession Number: 10879795
Digital Object Identifier: 10.1109/TEVC.2009.2011992
First Published: 2009-08-07
Current Version Published: 2009-09-22
Abstract
Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members search either for ldquofindingrdquo (producer) or for ldquojoiningrdquo (scrounger) opportunities. Based on this framework, concepts from animal searching behavior, e.g., animal scanning mechanisms, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. When tested against benchmark functions, in low and high dimensions, the GSO algorithm has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems. The GSO algorithm is also applied to train artificial neural networks. The promising results on three real-world benchmark problems show the applicability of GSO for problem solving.
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