Open AccessMetaheuristic algorithms in optimization and its application: a review
Author(s) -
Shahab Wahhab Kareem,
Kurdistan Wns Hama Ali,
Shavan Askar,
Farah Sami Xoshaba,
Roojwan Hawezi
Publication year - 2022
Publication title -
jaree (journal on advanced research in electrical engineering)
Language(s) - English
Resource type - Journals
eISSN - 2580-0361
pISSN - 2579-6216
DOI - 10.12962/jaree.v6i1.216
Subject(s) - metaheuristic , parallel metaheuristic , ant colony optimization algorithms , simulated annealing , computer science , differential evolution , meta optimization , particle swarm optimization , mathematical optimization , swarm intelligence , genetic algorithm , algorithm , multi swarm optimization , machine learning , mathematics
Metaheuristic algorithms are computational intelligence paradigms especially used for solving different optimization issues. Metaheuristics examine a collection of solutions otherwise really be wide to be thoroughly addressed or discussed in any other way. Metaheuristics can be applied to a wide range of problems because they make accurate predictions in any optimization situation. Natural processes such as the fact of evolution in Natural selection behavioral genetics, ant behaviors in genetics, swarm behaviors of certain animals, annealing in metallurgy, and others motivate metaheuristics algorithms. The big cluster search algorithm is by far the most commonly used metaheuristic algorithm. The principle behind this algorithm is that it begins with an optimal state and then uses heuristic methods from the community search algorithm to try to refine it. Many metaheuristic algorithms in diverse environments and areas are examined, compared, and described in this article. Such as Genetic Algorithm (GA), ant Colony Optimization Algorithm (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and etc. Finally, show the results of each algorithm in various environments were addressed.