Resumen
This paper presents an optimization algorithm named Random Explosion Algorithm (REA). The fundamental idea of this algorithm is based on a simple concept of the explosion of an object. This object is commonly known as a particle: when exploded, it will randomly disperse fragments around the particle within the explosion radius. The fragment that will be considered as a search agent will fill the local space and search that particular region for the best fitness solution. The proposed algorithm was tested on 23 benchmark test functions, and the results are validated by a comparative study with eight well-known algorithms, which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Multi-Verse Optimizer (MVO), Moth Flame Optimizer (MFO), Firefly Algorithm (FA), and Sooty Tern Optimization Algorithm (STOA). After that, the algorithm was implemented and analyzed for a quadrotor control application. Similarly, a comparative study with the other algorithms stated was done. The findings reveal that the REA can yield very competitive results. It also shows that the convergence analysis has proved that the REA can converge more quickly toward the global optimum than the other metaheuristic algorithms. For the control application result, the REA controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).