Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control

Juan Carlos Vazquez,Fevrier Valdez,Fernando Gaxiola

doi:10.1155/2018/1274969

Juan Carlos Vazquez, Fevrier Valdez + Show 1 more

Open Access

https://doi.org/10.1155/2018/1274969

Copy DOI

Abstract

A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.

Highlights

In automating the design of fuzzy controllers, the use of metaheuristic algorithms has been amply proposed
Ant Colony Optimization (ACO) is a metaheuristic algorithm based on the collaboration of artificial ants in a colony to search for an optimal solution for complex problems
In ACO algorithms, the collaboration between the artificial ants is an important part, and this consists in assigning the computational resources to artificial ants by indirect communication mediated by the environment

Summary

Introduction

In automating the design of fuzzy controllers, the use of metaheuristic algorithms has been amply proposed. The proposed approach is based on the swarm intelligence models [1] that perform research of the collective behavior in decentralized schemes; two swarm intelligence models are utilized in this paper: the Ant Colony Optimization (ACO) [2] and Particle Swarm Optimization (PSO) [3, 4] algorithms. ACO is a metaheuristic algorithm based on the collaboration of artificial ants in a colony to search for an optimal solution for complex problems. The fuzzy logic control is utilized in ill-defined complex process that can be operated by a trained human without knowing the dynamics of the system. In a FLC, the basic idea consists in utilizing the knowledge of an expert operator for the construction of the FLC that performs the control for a system; the input-output variables for the system are represented by fuzzy rules (IF-).

Methods

Results

Conclusion