Comparison of the performance of different metaheuristic optimization algorithms

Resumo

Metaheuristic algorithms are powerful tools for solving optimization problems. With the advancement
of computational technology, many metaheuristic algorithms were developed to solve optimization problems
quickly and accurately. Within this context, in this paper five of the main metaheuristic optimization algorithms
developed in recent decades – Particle Swarm Optimization (PSO), Harmony Search (HS), Firefly Algorithm (FA),
Search Group Algorithm (SGA) and Whale Optimization Algorithm (WOA) – had their performance compared in
the solution of problems involving the optimization of benchmark functions and the optimization of trusses in
which the design variables are the cross-sectional areas. Each algorithm was evaluated in terms of precision,
computational time of operation and standard deviation among the results obtained after many executions. With
the results obtained, the effectiveness of the five algorithms has been proven, although the older algorithms have
a slightly lower performance. In most problems, the best results were achieved through the WOA or the SGA.