@Article{cmc.2023.035911,
AUTHOR = {Dongyang Li, Shiyu Du, Yiming Zhang, Meiting Zhao},
TITLE = {Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {75},
YEAR = {2023},
NUMBER = {2},
PAGES = {2775--2803},
URL = {http://www.techscience.com/cmc/v75n2/51969},
ISSN = {1546-2226},
ABSTRACT = {Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science
and engineering fields. They are popular and have broad applications owing
to their high efficiency and low complexity. These algorithms are generally
based on the behaviors observed in nature, physical sciences, or humans. This
study proposes a novel metaheuristic algorithm called dark forest algorithm
(DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest
civilization, advanced civilization, normal civilization, and low civilization.
Each civilization has a unique way of iteration. To verify DFA’s capability,
the performance of DFA on 35 well-known benchmark functions is compared
with that of six other metaheuristic algorithms, including artificial bee colony
algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm,
grasshopper optimization algorithm, and whale optimization algorithm. The
results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when
solving high dimensional problems. DFA is applied to five engineering projects
to demonstrate its applicability. The results show that the performance of
DFA is competitive to that of current well-known metaheuristic algorithms.
Finally, potential upgrading routes for DFA are proposed as possible future
developments.},
DOI = {10.32604/cmc.2023.035911}
}