AI Summary & Key Takeaways
AI InsightThe results show that the cited methods show different performance depending on the type of the optimization problem but overally BOA and TLBO outperform the other algorithms on non-constrained and constrained problems, respectively.
📌 3 Key Takeaways
Abstract
Metaheuristic algorithms belong to the non-gradient based optimization methods. Accomplished studies in this area reveal that each of these methods mostly has its own affirmative and inconvenient aspects. So that, one might provide a high level of exploration while the other can perform a great level of exploitation. Thus, selecting the proper and efficient algorithm for a problem can highly affect both the convergence rate and the accuracy level. There are several different metaheuristic algorithms have been announced in the technical literature in the last decade. Therefore, performing an objective comparative assessment over some of these methods can provide a fundamental and fair attitude for researchers either to select an algorithm which is more fitted with their target(s) or to develop even more efficient methods. So, the current investigation deals with evaluating and comparing of five different metaheuristic techniques emerged from ten years ago up to now. The selected methods can be sorted chronologically as Firefly Algorithm (FA), Teaching and Learning Based Algorithm (TLBO), Drosophila Food Search (DSO) method, Ions Motion Optimization (IMO) and Butterfly Optimization Algorithm (BOA). Different properties of these algorithms as convergence rate, diversity variation, complexity and accuracy level of the final solutions are compared on both constrained and non-constrained optimization problems include mathematical functions, mechanical and structural problems. The results show that the cited methods show different performance depending on the type of the optimization problem but overally BOA and TLBO outperform the other algorithms on non-constrained and constrained problems, respectively.
Özet
Metaheuristic algorithms belong to the non-gradient based optimization methods. Accomplished studies in this area reveal that each of these methods mostly has its own affirmative and inconvenient aspects. So that, one might provide a high level of exploration while the other can perform a great level of exploitation. Thus, selecting the proper and efficient algorithm for a problem can highly affect both the convergence rate and the accuracy level. There are several different metaheuristic algorithms have been announced in the technical literature in the last decade. Therefore, performing an objective comparative assessment over some of these methods can provide a fundamental and fair attitude for researchers either to select an algorithm which is more fitted with their target(s) or to develop even more efficient methods. So, the current investigation deals with evaluating and comparing of five different metaheuristic techniques emerged from ten years ago up to now. The selected methods can be sorted chronologically as Firefly Algorithm (FA), Teaching and Learning Based Algorithm (TLBO), Drosophila Food Search (DSO) method, Ions Motion Optimization (IMO) and Butterfly Optimization Algorithm (BOA). Different properties of these algorithms as convergence rate, diversity variation, complexity and accuracy level of the final solutions are compared on both constrained and non-constrained optimization problems include mathematical functions, mechanical and structural problems. The results show that the cited methods show different performance depending on the type of the optimization problem but overally BOA and TLBO outperform the other algorithms on non-constrained and constrained problems, respectively.