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- Title
Self-adaptive fruit fly optimizer for global optimization.
- Authors
Sang, Hong-Yan; Pan, Quan-Ke; Duan, Pei-yong
- 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.
- Subjects
GLOBAL optimization; FRUIT flies; MATHEMATICAL optimization; SELF-adaptive software; SWARM intelligence; EVOLUTIONARY algorithms
- Publication
Natural Computing, 2019, Vol 18, Issue 4, p785
- ISSN
1567-7818
- Publication type
Article
- DOI
10.1007/s11047-016-9604-z