Paper Info
Title
An Improved Chaotic Optimization Algorithm Applied to a DC Electrical Motor Modeling.
Abstract
The chaos-based optimization algorithm (COA) is a method to optimize possibly nonlinear complex functions of several variables by chaos search. The main innovation behind the chaos-based optimization algorithm is to generate chaotic trajectories by means of nonlinear, discrete-time dynamical systems to explore the search space while looking for the global minimum of a complex criterion function. The aim of the present research is to investigate the numerical properties of the COA, both on complex optimization test-functions from the literature and on a real-world problem, to contribute to the understanding of its global-search features. In addition, the present research suggests a refinement of the original COA algorithm to improve its optimization performances. In particular, the real-world optimization problem tackled within the paper is the estimation of six electro-mechanical parameters of a model of a direct-current (DC) electrical motor. A large number of test results prove that the algorithm achieves an excellent numerical precision at a little expense in the computational complexity, which appears as extremely limited, compared to the complexity of other benchmark optimization algorithms, namely, the genetic algorithm and the simulated annealing algorithm.
Year
DOI
Venue
2017
10.3390/e19120665
ENTROPY
Keywords
Field
DocType
chaotic systems,non-smooth optimization,global optimization,DC electrical motor modeling
Simulated annealing,Mathematical optimization,Nonlinear system,Global optimization,Dynamical systems theory,Chaotic,Optimization problem,Genetic algorithm,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
19
12
1099-4300
Citations 
PageRank 
References 
2
0.38
8
Authors
2
Name
Order
Citations
PageRank
Simone Fiori149452.86
Ruben Di Filippo220.38