Solution of design optimization problems via metaheuristic search methods
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Date
2022
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Abstract
Metaheuristic algorithms inspired by natural phenomena are frequently used in
solving optimization problems recently. Just as every problem has its characteristics,
every algorithm has its unique structure. Therefore, problem-specific algorithm
selection is an important issue. In addition, metaheuristic algorithms are very open
to development. Therefore, improved/modified versions of algorithms are common.
Working with benchmarking problems and engineering design problems is the best
way to compare the performance and reliability of metaheuristic algorithms. In this
study, the performances of firefly (FA), particle swarm optimization (PSO), bat
algorithm (BA), ant colony optimization (ACO), glow worms (GSO), and hunting
search (HuS) algorithms are compared. An enhanced version of the firefly algorithm
(RFA) is also recommended and included in the comparison. A benchmark and five
engineering design problems were selected for comparison purposes. All algorithms
are set to twenty thousand iterations. The results show that the metaheuristic
algorithm that gives the best results varies according to the nature of the problem.
Moreover, although it does not change the ranking of the algorithm that gives the
best result according to the problem, it shows that RFA gives better results in every
problem than FA.