A survey on pioneering metaheuristic algorithms between 2019 and 2024
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915120771235840 |
|---|---|
| author | Dokeroglu, Tansel Canturk, Deniz Kucukyilmaz, Tayfun |
| author_facet | Dokeroglu, Tansel Canturk, Deniz Kucukyilmaz, Tayfun |
| contents | This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the process of selecting and assessing the most effective solutions for complex optimization challenges. Our evaluation centers on pivotal criteria, including annual citation metrics, the breadth of the addressed problem types, source code availability, user friendly parameter configurations, innovative mechanisms and operators, and approaches designed to mitigate traditional metaheuristic issues such as stagnation and premature convergence. We further explore recent high impact applications of the past six years' most influential 23 metahueristic algorithms, shedding light on their advantages and limitations, while identifying challenges and potential avenues for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A survey on pioneering metaheuristic algorithms between 2019 and 2024 Dokeroglu, Tansel Canturk, Deniz Kucukyilmaz, Tayfun Neural and Evolutionary Computing Artificial Intelligence A.1; F.2; I.2 This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the process of selecting and assessing the most effective solutions for complex optimization challenges. Our evaluation centers on pivotal criteria, including annual citation metrics, the breadth of the addressed problem types, source code availability, user friendly parameter configurations, innovative mechanisms and operators, and approaches designed to mitigate traditional metaheuristic issues such as stagnation and premature convergence. We further explore recent high impact applications of the past six years' most influential 23 metahueristic algorithms, shedding light on their advantages and limitations, while identifying challenges and potential avenues for future research. |
| title | A survey on pioneering metaheuristic algorithms between 2019 and 2024 |
| topic | Neural and Evolutionary Computing Artificial Intelligence A.1; F.2; I.2 |
| url | https://arxiv.org/abs/2501.14769 |