Table Of ContentStudies in Computational Intelligence 854
Erik Cuevas
Jorge Gálvez
Omar Avalos
Recent
Metaheuristics
Algorithms
for Parameter
Identification
Studies in Computational Intelligence
Volume 854
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
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á
Erik Cuevas Jorge G lvez
(cid:129) (cid:129)
Omar Avalos
Recent Metaheuristics
Algorithms for Parameter
fi
Identi cation
123
ErikCuevas Jorge Gálvez
CUCEI CUCEI
Universidad deGuadalajara Universidad deGuadalajara
Guadalajara, Mexico Guadalajara, Mexico
OmarAvalos
CUCEI
Universidad deGuadalajara
Guadalajara, Mexico
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-030-28916-4 ISBN978-3-030-28917-1 (eBook)
https://doi.org/10.1007/978-3-030-28917-1
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Preface
Sinceancienttimes,humanshaveusedrulesofthumbandstrategiesextractedfrom
other domains to solve several conflicting problems. Much of these methods have
been adopted in different areas such as economy, engineering. These
problem-solvingtechniquesarereferredundertheconceptofheuristics.Ingeneral,
heuristics are specially considered in those situations where there is insufficient or
incomplete information about the problem to be solved. As several problems pre-
sentthischaracteristic,heuristics-basedtechniquesbecamequitepopularinthelast
years.
On the other hand, metaheuristics imply a high-level heuristics in which the
problem-solving strategy is more general and adaptable to multiple contexts.
Metaheuristic methods perform better than simple heuristics, since their mecha-
nisms are guided for information (or knowledge) embedded within the
problem-solving process. Metaheuristic methods use as inspiration our scientific
understanding of biological, natural, or social systems, which at some level of
abstraction can be represented as optimization processes. They intend to serve as
general-purpose easy-to-use optimization techniques capable of reaching globally
optimal or at least nearly optimal solutions. In their operation, searcher agents
emulateagroupofbiologicalorsocialentitieswhichinteractwitheachotherbased
on specialized operators that model a determined biological or social behavior.
Theseoperatorsareappliedtoapopulationofcandidatesolutions(individuals)that
are evaluated with respect to an objective function. Thus, in the optimization
processindividualpositionsaresuccessivelyattractedtotheoptimalsolutionofthe
system to be solved.
There exist several features that clearly appear in most of the metaheuristic
approaches, such as the use of diversification to force the exploration of regions
of the search space, rarely visited until now, and the use of intensification or
exploitation,toinvestigatethoroughlysomepromisingregions.Anotherinteresting
feature is the use of memory to store the best solutions encountered. For these
reasons, metaheuristics methods quickly became popular among researchers to
solve from simple to complex optimization problems in different areas.
v
vi Preface
Most of the problems in science, engineering, economics, and life can be
translatedasanoptimizationorasearchproblem.Accordingtotheircharacteristics,
some problems can be simple that can be solved by traditional optimization
methods based on mathematical analysis. However, most of the problems of
practical importance such as system identification, parameter estimation, energy
systems represent conflicting scenarios so that they are very hard to be solved by
using traditional approaches. Under such circumstances, metaheuristic algorithms
have emerged as the best alternative to solve this kind of complex formulations.
Therefore, metaheuristics techniques have consolidated as a very active research
subjectinthelasttenyears.Duringthistime,variousnewmetaheuristicapproaches
have been introduced. They have been experimentally examined on a set of arti-
ficial benchmark problems and in a large number of practical applications.
Although metaheuristic methods represent one of the most exploited research
paradigms in computational intelligence, there are a large number of open chal-
lenges in the area of metaheuristics. They range from premature convergence,
inability to maintain population diversity, and the combination of metaheuristics
withotheralgorithmicschemes,towardextendingtheavailabletechniquestotackle
ever more difficult problems.
Amongtheengineeringproblems,identificationsystemsofprocesses,whichare
nonlinear in nature, represent a challenging formulation. In general, identification
systems refer to methods which allow to estimate the parameters that mathemati-
cally model a certain process. From an optimization perspective, identification
systems are considered extremely complex due to their nonlinearity, discontinuity,
andhighmultimodality.Thesecharacteristicsmakedifficulttosolvethembyusing
traditional optimization techniques. In the last years, researchers, engineers, and
practitioners in identification systems and modeling have faced problems of
increasingcomplexity.Theseproblemscanbe stated asoptimizationformulations.
Under these circumstances, an objective function is defined to evaluate the quality
of each candidate solution composed of the problem parameters. Then, an opti-
mization method is used to find the best solution that minimizes/maximizes the
objective function.
Numerousbookshavebeenpublishedtackinginaccountanyofthemostwidely
known metaheuristic methods, namely simulated annealing, tabu search, evolu-
tionary algorithms, ant colony algorithms, particle swarm optimization or differ-
ential evolution, but attempts to consider the discussion of new alternative
approachesarealwaysscarce.Initialmetaheuristicschemesmaintainintheirdesign
several limitations such as premature convergence and inability to maintain pop-
ulation diversity. Recent metaheuristic methods have addressed these difficulties
providing in general better results. Many of these novel metaheuristic approaches
have also been lately introduced. In general, they propose new models and inno-
vative algorithm combinations for producing an adequate exploration and
exploitationoflargesearchspacesconsideringasignificantnumberofdimensions.
Most of the new metaheuristic algorithms present promising results. Nevertheless,
they arestill intheirinitial stage.To growandattaintheircompletepotential,new
metaheuristicmethodsmustbeappliedinagreatvarietyofproblemsandcontexts,
Preface vii
so that they do not only perform well in their reported sets of optimization prob-
lems, but also in new complex formulations. The only way to accomplish this is
making possible the transmission and presentation of these methods in different
technical areas as optimization tools. In general, once a researcher, engineer, or
practitioner recognizes a problem as a particular instance of a more generic class,
he/she can select one of the different metaheuristic algorithms that guarantee an
expected optimization performance. Unfortunately, the set of options are concen-
tratedinalgorithmswhosepopularityandhighproliferationarebetterthanthenew
developments.
The excessive publication ofdevelopments based on thesimple modificationof
popularmetaheuristicmethodspresentsanimportantdisadvantage:Theyavoidthe
opportunity to discover new techniques and procedures which can be useful to
solve problems formulatedbytheacademic andindustrial communities.Inthelast
years, several promising metaheuristic methods that consider very interesting
concepts and operators have been introduced. However, they seem to have been
completely overlooked in the literature, in favor of the idea of modifying,
hybridizing, or restructuring popular metaheuristic approaches.
The first goal of this book is to present advances that discuss new alternative
metaheuristic developments which have proved to be effective in their application
to several complex problems. The book considers different new metaheuristic
methods and their practical applications. This structure is important to us, because
we recognize this methodology as the best way to assist researchers, lecturers,
engineers, and practitioners in the solution of their own optimization problems.
The second goal of this book is to bridge the gap between recent metaheuristic
techniques with interesting identification system methods that profit on the con-
venient properties of metaheuristic schemes. To do this, at each chapter we
endeavor to explain basic ideas of the proposed applications in ways that can be
understood by readers who may not possess the necessary backgrounds on either
ofthefields.Therefore,identificationsystemsandenergypractitionerswhoarenot
researchers in metaheuristics will appreciate that the techniques discussed are
beyond simple theoretical tools since they have been adapted to solve significant
problems that commonly arise on such areas. On the other hand, members of the
metaheuristic community can learn the way in which system identification and
energy problems can be translated into optimization tasks.
This book has been structured so that each chapter can be read independently
from the others. Chapter 1 describes the main characteristics and properties of
metaheuristic methods. This chapter concentrates on elementary concepts of
metaheuristic. Readers that are familiar with metaheuristic algorithms may wish to
skip this chapter.
In Chap. 2, an algorithm for the optimal parameter identification of induction
motors is presented. In the identification, the parameter estimation process is
transformed into a multidimensional optimization problem where the internal
parameters of the induction motor are considered as decision variables. Under this
approach,thecomplexityoftheoptimizationproblemtendstoproducemultimodal
error surfaces for which their cost functions are significantly difficult to minimize.
viii Preface
To determine the parameters, the presented scheme uses a recent metaheuristic
method called the gravitational search algorithm (GSA). Different to the most of
existentevolutionaryalgorithms,GSApresentsabetterperformanceinmultimodal
problems,avoidingcritical flawssuchastheprematureconvergencetosuboptimal
solutions. Numerical simulations have been conducted on several models to show
the effectiveness of the proposed scheme.
Chapter 3 presents an improved version of the crow search algorithm
(CSA)methodtosolvecomplexoptimizationproblemsofenergy.Intheimproved
algorithm, two features of the original CSA are modified: (I) the awareness prob-
ability (AP) and (II) the random perturbation. With the purpose to enhance the
exploration–exploitation ratio, the fixed awareness probability (AP) value is
replaced (I) by a dynamic awareness probability (DAP), which is adjusted
accordingtothefitnessvalueofeachcandidatesolution.TheLévyflightmovement
is also incorporated to enhance the search capacities of the original random per-
turbation (II) of CSA. In order to evaluate its performance, the algorithm has been
tested in a set offour optimization problems which involve induction motors and
distribution networks. The results demonstrate the high performance of the pro-
posed method when it is compared with other popular approaches.
InChap.4,acomparativestudybetweenmetaheuristictechniquesusedforsolar
cells parameter estimation is presented. The comparison evaluates the solar cell
models of one diode, two diodes, and three diodes. In the analysis, the solar cell
models are evaluated considering different operation conditions. Experimental
results obtained during the comparison are also statistically validated.
Chapter 5 considers a nonlinear system identification method based on the
Hammerstein model. In the scheme, the system is modeled through the adaptation
of an adaptive network-based fuzzy inference system (ANFIS) scheme, taking
advantageofthesimilitudebetweenitandtheHammersteinmodel.Toidentifythe
parameters of the modeled system, the approach uses a recent nature-inspired
method called the gravitational search algorithm (GSA). Different to most of
existent optimization algorithms, GSA delivers a better performance in complex
multimodalproblems,avoidingcriticalflawssuchastheprematureconvergenceto
suboptimal solutions. To show the effectiveness of the proposed scheme, its
modeling accuracy has been compared with other popular evolutionary computing
algorithms through numerical simulations on different complex models.
In Chap. 6, a methodology to implement human-knowledge-based optimization
strategies is presented. In the scheme, a Takagi-Sugeno Fuzzy inference system is
used to reproduce a specific search strategy generated by a human expert.
Therefore, the number of rules and its configuration only depends on the expert
experience without considering any learning rule process. Under these conditions,
each fuzzy rule represents an expert observation that models the conditions under
which candidate solutions are modified in order to reach the optimal location. To
exhibit the performance and robustness of the method, a comparison to other
well-known optimization methods is conducted. The comparison considers several
standard benchmark functions which are typically found in the scientific literature.
The results suggest a high performance of the proposed methodology.
Preface ix
Chapter 7 presents a recent metaheuristic algorithm called Neighborhood-based
Consensus for Continuous Optimization (NCCO). NCCO is based on typical
processespresentinmulti-agentsystems,suchaslocalconsensusformulationsand
reactive responses. These operations are conducted by using appropriate operators
that are applied in each evolutionary stage. A traditional metaheuristic algorithm
considers in its operation the application of every operator without examining its
finalimpact inthesearchingprocess.Incontrasttoothermetaheuristictechniques,
the proposed method uses additional operators to avoid the undesirable effects
produced by the over-exploitation or suboptimal exploration of conventional
operations. In order to illustrate the performance and accuracy of the proposed
NCCO approach, it is compared to several well-known, state-of-the-art algorithms
over a set of benchmark functions, and real-world design applications. The
experimental results demonstrate that NCCO’s performance is superior to the test
algorithms.
Chapter 8 presents a metaheuristic algorithm in which knowledge extracted
during its operation is employed to guide its search strategy. In the approach, a
self-organizing map (SOM) is used as extracting knowledge technique to identify
the promising areas through the reduction of the search space. Therefore, in each
generation, the scheme uses a subset of the complete group of generated solutions
seensofartotraintheSOM.Oncetrained,theneuralunitfromtheSOMlatticethat
corresponds to the best solution is identified. Then, by using local information of
thisneuralunitanentirepopulationofcandidatesolutionsisproduced.Withtheuse
oftheextractedknowledge,thenewapproachimprovestheconvergencetodifficult
high multimodal optima by using a reduced number of function evaluations. The
performance of our approach is compared to several state-of-the-art optimization
techniques considering a set of well-known functions and three real-world engi-
neering problems. The results validate that the presented method reaches the best
balance regarding accuracy and computational cost over its counterparts.
Guadalajara, Mexico Erik Cuevas
Jorge Gálvez
Omar Avalos
