Abstract
This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.
Highlights
In recent years, a significant amount of research has focused on solving optimization problems without any prior knowledge [1,2]
The objective of this work is to analyze the behavior of three bio-inspired optimization algorithms for searching for optimal solutions for a multimodal problem as represented by the Gaussian Adaptive Proportional, Integral and Derivative (GAPID) control strategy applied to typical power supplies of medical equipment with the Buck converter topology
We address the Integral Absolute Error (IAE), following the premises defined by previous investigations, which must be minimized [17,20]
Summary
A significant amount of research has focused on solving optimization problems without any prior knowledge [1,2]. Due to the multiple characteristics of real world problems, such as non-linearity, discontinuity, multimodality, non-differentiability, and so on, traditional mathematical techniques based on derivatives of the gradient may be insufficient for the posed challenges [3]. Ant Colony Optimization (ACO) [11], among others, provided sufficient evidence of efficiency and effectiveness in finding the optimal solutions to complex optimization problems [12,13,14]. When dealing with optimization problems, the agents in metaheuristics roam in the search space to obtain good solutions using experimental and local information [15]. The objective of this work is to analyze the behavior of three bio-inspired optimization algorithms for searching for optimal solutions for a multimodal problem as represented by the Gaussian Adaptive Proportional, Integral and Derivative (GAPID) control strategy applied to typical power supplies of medical equipment with the Buck converter topology
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