TY - JOUR
T1 - A bio-inspired evolutionary algorithm
T2 - Allostatic optimisation
AU - Osuna-Enciso, Valentín
AU - Cuevas, Erik
AU - Oliva, Diego
AU - Sossa, Humberto
AU - Pérez-Cisneros, Marco
N1 - Publisher Copyright:
Copyright © 2016 Inderscience Enterprises Ltd.
PY - 2016
Y1 - 2016
N2 - Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research has been devoted to find new and better optimisation methods. On the other hand, allostasis is a medical term recently coined which explains how the configuration of the internal state (IS) in different organs allows reaching stability when an unbalance condition is presented. In this paper, a novel biologically-inspired algorithm called allostatic optimisation (AO) is proposed for solving optimisation problems. In AO, individuals emulate the IS of different organs. In the approach, each individual is improved by using numerical operators based on the biological principles of the allostasis mechanism. The proposed method has been compared to other well-known optimisation algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
AB - Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research has been devoted to find new and better optimisation methods. On the other hand, allostasis is a medical term recently coined which explains how the configuration of the internal state (IS) in different organs allows reaching stability when an unbalance condition is presented. In this paper, a novel biologically-inspired algorithm called allostatic optimisation (AO) is proposed for solving optimisation problems. In AO, individuals emulate the IS of different organs. In the approach, each individual is improved by using numerical operators based on the biological principles of the allostasis mechanism. The proposed method has been compared to other well-known optimisation algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
KW - Allostasis
KW - Bio-inspired computation
KW - Evolutionary algorithms
KW - Optimisation
UR - http://www.scopus.com/inward/record.url?scp=84971372814&partnerID=8YFLogxK
U2 - 10.1504/IJBIC.2016.076633
DO - 10.1504/IJBIC.2016.076633
M3 - Artículo
SN - 1758-0366
VL - 8
SP - 154
EP - 169
JO - International Journal of Bio-Inspired Computation
JF - International Journal of Bio-Inspired Computation
IS - 3
ER -