Metaheuristics in large-scale global continues optimization: A survey
By Sedigheh Mahdavi, Mohammad Ebrahim Shiri, Shahryar Rahnamayan. Information Sciences, 2015
452 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/0f2dd167244acd405041db0505db8084e78e3431
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Swagatam Das
- Seyedali Mirjalili
- Ponnuthurai Nagaratnam Suganthan
- Leandro dos S. Coelho
- Francisco Herrera
- Xin-She Yang
- Kusum Deep
- Yaochu Jin
In Industrial and Manufacturing Engineering
- Leandro dos S. Coelho
- Juliano Pierezan
- Kusum Deep
- Yusuke Nojima
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Kanchan Rajwar
- Hisao Ishibuchi
Citations in the corpus
- Bio-inspired computation: Where we stand and what's next (2019, background): “[289] for a more detailed description of the evolution of the LSGO area.”
- DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (2017, unknown): “This motivated the development of a wide range of scalable algorithms in the classic mathematical programming domain [12, 13] as well as metaheuristics [14, 15].”
- An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, methodology): “Scalability of LSGO methods becomes a critical prerequisite, with significant implications for future study (Mahdavi et al. 2015).”
- Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems (2018, background): “As the classic Newtonian and gradient-based techniques are not suitable to perform global optimization, a exponential growth of researches on new stochastic metaheuristics for handling global optimization is noted in recent literature [1]–[3].”
- Metaheuristic research: a comprehensive survey (2018, background): “Mahdavi et al. (2015) foresee the design of decomposition methods, with great performance and accuracy, as potential research areawhile solving real-world imbalanced problems with large decision variables.”
- A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, background): “Recently, a wide variety of single-objective many-variable optimizers have been proposed in the literature (see for instance the review by Mahdavi et al. [1]).”