Open Access

ARTICLE

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Dongyang Li¹, Shiyu Du^2,*, Yiming Zhang², Meiting Zhao³

1 Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315000, China
2 Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315000, China
3 School of Material and Chemical Engineering, Ningbo University, Ningbo, 315000, China

* Corresponding Author: Shiyu Du. Email:

Computers, Materials & Continua 2023, 75(2), 2775-2803. https://doi.org/10.32604/cmc.2023.035911

Received 09 September 2022; Accepted 30 December 2022; Issue published 31 March 2023

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.

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Keywords

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