@ARTICLE{9064786,
author={H. {Li} and X. {Liu} and Z. {Huang} and C. {Zeng} and P. {Zou} and Z. {Chu} and J. {Yi}},
journal={IEEE Access}, 
title={Newly Emerging Nature-Inspired Optimization - Algorithm Review, Unified Framework, Evaluation, and Behavioural Parameter Optimization}, 
year={2020},
volume={8},
number={},
pages={72620-72649},
abstract={Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.},
keywords={Optimization;Taxonomy;Heuristic algorithms;Software algorithms;Classification algorithms;Benchmark testing;Licenses;Nature inspired optimization;meta-heuristics;unified framework;parameter optimization;meta-optimization},
doi={10.1109/ACCESS.2020.2987689},
ISSN={2169-3536},
month={},}