Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics: Theory and Applications

193 Pages·2015·7.482 MB·English

by  Oscar Castillo, Patricia Melin (eds.)

Studies in Computational Intelligence 574
Oscar Castillo
Patricia Melin Editors
Fuzzy Logic
Augmentation of
Nature-Inspired
Optimization
Metaheuristics
Theory and Applications
Studies in Computational Intelligence
Volume 574
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
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Oscar Castillo Patricia Melin
(cid:129)
Editors
Fuzzy Logic Augmentation
of Nature-Inspired
Optimization Metaheuristics
Theory and Applications
123
Editors
Oscar Castillo
Patricia Melin
Division ofGraduate Studiesand Research
Tijuana InstituteofTechnology
Tijuana
Baja California
Mexico
ISSN 1860-949X ISSN 1860-9503 (electronic)
ISBN 978-3-319-10959-6 ISBN 978-3-319-10960-2 (eBook)
DOI 10.1007/978-3-319-10960-2
LibraryofCongressControlNumber:2014951140
SpringerChamHeidelbergNewYorkDordrechtLondon
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Preface
We describe in this book, recent advances on fuzzy logic augmentation of
nature-inspired optimization metaheuristics and their application in areas, such as
intelligent control and robotics, pattern recognition, time series prediction, and
optimization of complex problems. The book is organized into two main parts,
which contain a group of papers around a similar subject. Part I consists of papers
with the main theme of theoretical aspects offuzzy logic augmentation of nature-
inspired optimization metaheuristics, which basically consists of papers that pro-
pose new optimization algorithms enhanced using fuzzy systems. Part II contains
papers with the main theme of application of optimization algorithms, which are
basically papers using nature-inspired techniques to achieve optimization of com-
plex optimization problems in diverse areas of application.
In the part of theoretical aspects offuzzy logic augmentation of nature-inspired
optimization metaheuristics, there are seven chapters that describe different con-
tributions that propose new models and concepts, which can be the considered as
the basis for enhancing nature-inspired algorithms with fuzzy logic. The aim of
using fuzzy logic is to provide dynamic adaptation capabilities to the optimization
algorithms,andthisisillustratedwiththecasesofthebatalgorithm,cuckoosearch,
and other methods. In the part of applications offuzzy nature-inspired algorithms
therearefivechaptersthatdescribedifferentcontributionsontheapplicationofthe
nature-inspired algorithms to solve complex optimization problems. The nature-
inspired methods include variations of ant colony optimization, particle swarm
optimization, the bat algorithm, as well as new nature inspired paradigms.
In conclusion, the edited book comprises papers on diverse aspects of fuzzy
logic augmentation of nature-inspired optimization metaheuristics and their appli-
cation in areas, such as intelligent control and robotics, pattern recognition, time
series prediction, and optimization of complex problems. There are theoretical
aspects as well as application papers.
Mexico, May 2014 Oscar Castillo
Patricia Melin
v
Contents
Part I Theory
Fuzzy Logic for Dynamic Parameter Tuning in ACO
and Its Application in Optimal Fuzzy Logic Controller Design . . . . . . 3
Héctor Neyoy, Oscar Castillo and José Soria
Fuzzy Classification System Design Using PSO
with Dynamic Parameter Adaptation Through Fuzzy Logic. . . . . . . . . 29
Frumen Olivas, Fevrier Valdez and Oscar Castillo
Differential Evolution with Dynamic Adaptation of Parameters
for the Optimization of Fuzzy Controllers. . . . . . . . . . . . . . . . . . . . . . 49
Patricia Ochoa, Oscar Castillo and José Soria
A New Bat Algorithm with Fuzzy Logic for Dynamical
Parameter Adaptation and Its Applicability to Fuzzy
Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Jonathan Pérez, Fevrier Valdez and Oscar Castillo
Optimization of Benchmark Mathematical Functions
Using the Firefly Algorithm with Dynamic Parameters . . . . . . . . . . . . 81
Cinthya Solano-Aragón and Oscar Castillo
Cuckoo Search via Lévy Flights and a Comparison
with Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Maribel Guerrero, Oscar Castillo and Mario García
A Harmony Search Algorithm Comparison with Genetic
Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Cinthia Peraza, Fevrier Valdez and Oscar Castillo
vii
viii Contents
Part II Applications
A Gravitational Search Algorithm for Optimization
of Modular Neural Networks in Pattern Recognition. . . . . . . . . . . . . . 127
Beatriz González, Fevrier Valdez, Patricia Melin
and German Prado-Arechiga
Ensemble Neural Network Optimization Using the Particle Swarm
Algorithm with Type-1 and Type-2 Fuzzy Integration
for Time Series Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Martha Pulido and Patricia Melin
Clustering Bin Packing Instances for Generating a Minimal Set
of Heuristics by Using Grammatical Evolution . . . . . . . . . . . . . . . . . . 151
Marco Aurelio Sotelo-Figueroa, Héctor José Puga Soberanes,
Juan Martín Carpio, Héctor J. Fraire Huacuja, Laura Cruz Reyes
and Jorge Alberto Soria Alcaraz
Comparative Study of Particle Swarm Optimization Variants
in Complex Mathematics Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Juan Carlos Vazquez, Fevrier Valdez and Patricia Melin
Optimization of Modular Network Architectures with a New
Evolutionary Method Using a Fuzzy Combination of Particle Swarm
Optimization and Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . 179
Fevrier Valdez
Part I
Theory
Fuzzy Logic for Dynamic Parameter
Tuning in ACO and Its Application
in Optimal Fuzzy Logic Controller Design
Héctor Neyoy, Oscar Castillo and José Soria
Abstract Ant Colony Optimization (ACO) is a population-based constructive
metaheuristic that exploits a form of past performance memory inspired by the
foraging behavior of real ants. The behavior of the ACO algorithm is highly
dependent on the values defined for its parameters. Adaptation and parameter
control are recurring themes in the field of bio-inspired algorithms. The present
paper explores a new approach of diversity control in ACO. The central idea is to
avoid or slow down full convergence through the dynamic variation of a certain
parameter. The performance of different variants of the ACO algorithm was
observedtochooseoneasthebasistotheproposedapproach.Aconvergencefuzzy
logic controller with the objective of maintaining diversity at some level to avoid
premature convergence was created. Encouraging results on several travelling
salesman problem (TSP) instances and its application to the design offuzzy con-
trollers, in particular the optimization of membership functions for a unicycle
mobile robot trajectory control are presented with the proposed method.
1 Introduction
ACO is inspired by the foraging behavior of ant colonies, and targets discrete
optimization problems [1].
The behavior of the ACO algorithm is highly dependent on the values defined
for its parameters and has an effect on its convergence. Often these are kept static
during the execution of the algorithm. Changing the parameters at runtime, at a
given time or depending on the search progress may improve the performance of
the algorithm [2–4].
Controlthedynamicsofconvergencetomaintainabalancebetweenexploration
and exploitation is critical for good performance.
H.Neyoy(cid:1)O.Castillo(&)(cid:1)J.Soria
TijuanaInstituteofTechnology,Tijuana,Mexico
e-mail:[email protected]
©SpringerInternationalPublishingSwitzerland2015 3
O.CastilloandP.Melin(eds.),FuzzyLogicAugmentationofNature-Inspired
OptimizationMetaheuristics,StudiesinComputationalIntelligence574,
DOI10.1007/978-3-319-10960-2_1