Intelligent Search of Values for a Controller Using the Artificial Bee Colony Algorithm to Control the Velocity of Displacement of a Robot

Abstract

The optimization is essential in the engineering area and, in conjunction with use of meta-heuristics, has had a great impact in recent years; this is because of its great precision in search of optimal parameters for the solution of problems. In this work, the use of the Artificial Bee Colony Algorithm (ABC) is presented to optimize the values for the variables of a proportional integral controller (PI) to observe the behavior of the controller with the optimized Ti and Kp values. It is proposed using a robot built using the MINDSTORMS version EV3 kit. The objective of this work is to demonstrate the improvement and efficiency of the controllers in conjunction with optimization meta-heuristics. In the results section, we observe that the results improve considerably compared to traditional methods. In this work, the main contribution is the implementation of an optimization algorithm (ABC) applied to a controller (PI), and the results are tested to control the movement of a robot. There are many papers where the kit is used in various domains such as education as well as research for science and technology tasks and some real-world problems by engineering scholars, showing the acceptable result.

Data-Driven Rock Strength Parameter Identification Using Artificial Bee Colony Algorithm

“…Zhao et al applied ABC to the back analysis in geotechnical engineering [34]. Villegas et al applied ABC to optimize the values for the variables of a proportional integral controller for observing the behavior of the controller [35]. Caraveo et al developed a modification of a bio-inspired algorithm based on ABC for optimizing fuzzy controllers [36].…”

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

“…Aspects of the metaheuristic method have been employed to optimize answer or fitness values when solving complex problems. Well-known metaheuristic algorithms include the genetic algorithm (GA) [39], genetic programming (GP), evolutionary programming (EP), particle swarm optimization (PSO) [40,41], simulated annealing (SA) [42], artificial bee colony (ABC) [43,44], and harmony search (HS) [45][46][47][48]. The HS algorithm is an evolutionary algorithm used to find fitness values based on the problem-solving principles of musicians.…”