Overview of Metaheuristic Algorithms

Authors

  • Saman M. Almufti Department of Computer Science, Nawroz University, Duhok, Kurdistan Region of Iraq
  • Awaz Ahmad Shaban Researcher
  • Zeravan Arif Ali Department of Information Technology Management, Duhok Polytechnic, Kurdistan Region, Iraq
  • Rasan Ismael Ali Researcher
  • Jayson A. Dela Fuente Northern Negros State College of Science Technology, Philippines

DOI:

https://doi.org/10.58429/pgjsrt.v2n2a144

Keywords:

Metaheuristics Algorithm, classifications, advantages and disadvantages, applications

Abstract

Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.

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Published

2023-04-29

How to Cite

M. Almufti, S., Ahmad Shaban, A., Arif Ali, Z., Ismael Ali, R., & A. Dela Fuente, J. (2023). Overview of Metaheuristic Algorithms. Polaris Global Journal of Scholarly Research and Trends, 2(2), 10–32. https://doi.org/10.58429/pgjsrt.v2n2a144

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