Metaheuristics: review and application

By Anupriya Gogna, Akash Tayal. Journal of experimental and theoretical artificial intelligence (Print), 2013

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e95310fd591e56ab5cab7925a72cb52b29ee602d

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

1. Seyedali Mirjalili
2. Shahrzad Saremi
3. Arpan Kumar Kar
4. Andrew Lewis
5. Li Yang
6. Abdallah Shami
7. Ali Asghar Heidari
8. Afeez Abidemi

In Industrial and Manufacturing Engineering

1. Li Yang
2. Abdallah Shami
3. Seyedali Mirjalili
4. Arpan Kumar Kar
5. Shahrzad Saremi
6. Andrew Lewis

In Management Science and Operations Research

1. Li Yang
2. Abdallah Shami
3. Seyedali Mirjalili
4. Arpan Kumar Kar
5. Shahrzad Saremi
6. Andrew Lewis

Citations in the corpus

• Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “AC CE PT ED M AN US CR IP T Figure 1: Development focus of bio-inspired algorithms Some reviews of metaheuristics algorithms (Yang, 2011; Gogna & Tayal, 2013) have been conducted, but these studies have focused mostly only genetic algorithm, ant colony optimization and neural networks as part of…”
• The Ant Lion Optimizer (2015, unknown): “In recent years metaheuristic algorithms have been used as primary techniques for obtaining the optimal solutions of real engineering design optimization problems [1–3].”
• Grasshopper Optimisation Algorithm: Theory and application (2017, background): “Due to their simplicity, gradient-free mechanism, high local optima avoidance, and considering problems as black boxes, nature-inspired algorithms have been applied widely in science and industry [34-36].”
• On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, methodology): “Metaheuristic algorithms [98] are a set of algorithms mainly inspired by biological theories and widely used for optimization problems.”
• Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “The acceptance and popularity of metaheuristic algorithms can be attributed to their concurrent and quite simple implementation, their ability to handle large complex problems at a reduced computational cost, and their wide usage in different areas of endeavor (Gogna and Tayal, 2013; Talbi, 2009).”
• SCA: A Sine Cosine Algorithm for solving optimization problems (2016, background): “e s t t a a c p of this paper is on the theory, the applications are not discussed further and interested readers are referred to [11,12].”