Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965-2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. Keywords: Intelligent optimization algorithms, soft computing, literature review, state-of-the-art, statistics.
Funding No funds!
Informed consent Informed consent was obtained from all individual participants included in the study.
Human and animal rights This paper does not contain any studies with human or animal subjects performed by any of the authors.
CRediT authorship contribution statement Ali Mohammadi: Conceptualization, Methodology, Resources, Software, Formal analysis, Data curation, Writing – original draft, Visualization, Software, Validation, Project administration, Writing – review & editing. Farid Sheikholeslam: Investigation, Supervision.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments The authors would like to thank the editors and reviewers for providing valuable comments and suggestions that helped to significantly improve the manuscript. They also thank all the supporters and colleagues in partner universities.
Ali Mohammadi sincerely thanks his family for their support and kindness during the preparation, submission, and acceptance of this prolific review/survey paper.
Data availability All data used for this research is available with the paper. Data is available at: https://github.com/ali-ece. Figures prepared in Microsoft Visio 2010.
For free access to the paper, click the following link: https://authors.elsevier.com/a/1i5nh3OWJ94rXc https://doi.org/10.1016/j.engappai.2023.106959