Laith Abualigah^1,2*

¹Faculty of Computer Sciences and Informatics, Amman Arab University, Jordan
²School of Computer Sciences, Universiti Sains Malaysia, Malaysia

*Corresponding Author: Laith Abualigah, Faculty of Computer Sciences and Informatics, Amman Arab University, Jordan and School of Computer Sciences, Universiti Sains Malaysia, Malaysia.

Received: July 15, 2021; Published: August 18, 2021

Recently, many optimizations algorithm has been proposed in the literature to solve various optimization problems either easy or even hard. In this paper, we discuss the ability of the current optimization algorithms to solve any optimization problem from the read-world. When we look into the current methods, we found many optimizers proved their ability to solve the problems.

Bibliography

1. Whitley D. “A genetic algorithm tutorial”. Statistics and Computing2 (1994): 65-85.
2. Abualigah, L., et al. “The arithmetic optimization algorithm”. Computer Methods in Applied Mechanics and Engineering 376 (2021): 113609.
3. Abualigah, L., et al. “Aquila Optimizer: A novel meta-heuristic optimization Algorithm”. Computers and Industrial Engineering 157 (2021): 107250.
4. Kennedy J and R Eberhart. “Particle swarm optimization”. in Proceedings of ICNN'95-international conference on neural networks. IEEE (1995).
5. Wolpert DH and WG Macready. “No free lunch theorems for optimization”. IEEE Transactions on Evolutionary Computation 1 (1997): 67-82.
6. Abualigah LMQ. “Feature selection and enhanced krill herd algorithm for text document clustering”. Springer (2019).
7. Abualigah L and AJ Dulaimi. “A novel feature selection method for data mining tasks using hybrid sine cosine algorithm and genetic algorithm”. Cluster Computing (2021): 1-16.
8. Abd Elaziz M., et al. “Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments”. Future Generation Computer Systems 124 (2021): 142-154.
9. Yousri D., et al. “Reliable applied objective for identifying simple and detailed photovoltaic models using modern metaheuristics: Comparative study”. Energy Conversion and Management 223 (2020): 113279.
10. Safaldin M., et al. “Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks”. Journal of Ambient Intelligence and Humanized Computing2 (2021): 1559-1576.
11. Abualigah L., et al. “Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications”. Engineering with Computers (2020): 1-27.
12. Abualigah L. “Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications”. Neural Computing and Applications7 (2021): 2949-2972.
13. Abualigah L and A Diabat. “A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications”. Neural Computing and Applications (2020): 1-24.
14. Abualigah L., et al. “A comprehensive survey of the harmony search algorithm in clustering applications”. Applied Sciences11 (2020): 3827.

Citation

Citation: Laith Abualigah. “Modern Optimization Problems and its Solutions Using a Variety of Meta-Heuristic Optimization Algorithms: Application, Description, Critical Points, and Potential Future Works". Acta Scientific Computer Sciences 3.9 (2021): 50-51.

Copyright

Copyright: © 2021 Laith Abualigah. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.