TY - JOUR
T1 - Artificial ecosystem-based optimization
T2 - a novel nature-inspired meta-heuristic algorithm
AU - Zhao, Weiguo
AU - Wang, Liying
AU - Zhang, Zhenxing
N1 - Funding Information:
This work was supported in part by Natural Science Foundation of Hebei Province of China (E2018402092, F2017402142) and Scientific Research Key Project of University of Hebei Province of China (ZD2017017).
PY - 2020/7/1
Y1 - 2020/7/1
N2 - A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo.
AB - A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo.
KW - Artificial ecosystem-based optimization
KW - Constrained problems
KW - Engineering design
KW - Global optimization
KW - Hydrogeological parameter
KW - Optimization algorithm
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U2 - 10.1007/s00521-019-04452-x
DO - 10.1007/s00521-019-04452-x
M3 - Article
AN - SCOPUS:85073930520
SN - 0941-0643
VL - 32
SP - 9383
EP - 9425
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 13
ER -