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

Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular broad applications owing to their high efficiency low complexity. These algorithms generally based on the behaviors observed nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest (DFA), which can yield improved results global problems. In DFA, population is divided into four groups: highest civilization, advanced normal civilization. Each civilization has unique way of iteration. To verify DFA’s capability, performance DFA 35 well-known benchmark functions compared with that six other including artificial bee colony algorithm, firefly grey wolf optimizer, harmony search grasshopper whale algorithm. The show provides solutions problems dimensions outperforms most when dimensional applied five projects demonstrate its applicability. competitive current algorithms. Finally, potential upgrading routes proposed possible future developments.