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Natural Computing

Self-adaptive fruit fly optimizer for global optimization

  • 03-01-2017
Published in:
Authors
Hong-Yan Sang
Quan-Ke Pan
Pei-yong Duan
Published in
Natural Computing | Issue 4/2019

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Abstract

A self-adaptive fruit fly optimization (SFFO) algorithm is presented for solving high-dimensional global optimization problems. Unlike the conventional self-adaptive swarm intelligence algorithms that try to modify the values of control parameters during the run by taking the actual search process into account, the proposed SFFO algorithm self-adaptively adjusts its search along an appropriate decision variable from its previous experience in generating promising solutions. The presented self-adaptive method significantly improves the intensive search capability of the fruit fly optimization algorithm around promising areas that are problem and search process dependent. Extensive computational simulations and comparisons are performed based on a set of 40 benchmark functions from the literature. The computational results show that the proposed SFFO is a new state-of-the-art algorithm for global optimization.

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Title
Self-adaptive fruit fly optimizer for global optimization
Authors
Hong-Yan Sang
Quan-Ke Pan
Pei-yong Duan
Publication date
03-01-2017
Publisher
Springer Netherlands
Published in
Natural Computing / Issue 4/2019
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-016-9604-z
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