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Osuna, Valentín; Cuevas, Erik; Sossa, Humberto
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1 Citations
Acute lymphoblastic leukemia is a blood cancer that can be cured if it is detected at early stages; however, the analysis of smear blood by a human expert is tired and subject to errors. In such a sense, diagnostic of the disease is costly and time consuming. Considering that situation, several automatic segmentation methods have been proposed, some of them containing combinations of classic image analysis tools, as thresholding, morphology, color segmentation and active contours, only to mention some. In this paper is proposed the use of Hellinger distance as an alternative to Euclidean distance in order to estimate a Gaussian functions mixture that better fits a graylevel histogram of blood cell images. Two evolutionary methods (Differential Evolution and Artificial Bee Colony) are used to perform segmentation based on histogram information and an estimator of minimum distance. The mentioned techniques are compared with classic Otsu’s method by using a qualitative measure of the resulting segmentation and groundtruth images. Experimental results show that the three methods performed almost in a similar fashion, but the evolutionary ones evaluate almost 75 % less the objective function compared with Otsu’s. Also, was found that the use of a minimum distance estimator constructed with Hellinger distance and evolutionary techniques is robust and does not need a penalization factor as the needed when an Euclidean distance is used.
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Fausto, Fernando; ReynaOrta, Adolfo; Cuevas, Erik; Andrade, Ángel G.; PerezCisneros, Marco
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Natureinspired metaheuristics comprise a compelling family of optimization techniques. These algorithms are designed with the idea of emulating some kind natural phenomena (such as the theory of evolution, the collective behavior of groups of animals, the laws of physics or the behavior and lifestyle of human beings) and applying them to solve complex problems. Natureinspired methods have taken the area of mathematical optimization by storm. Only in the last few years, literature related to the development of this kind of techniques and their applications has experienced an unprecedented increase, with hundreds of new papers being published every single year. In this paper, we analyze some of the most popular natureinspired optimization methods currently reported on the literature, while also discussing their applications for solving realworld problems and their impact on the current literature. Furthermore, we open discussion on several research gaps and areas of opportunity that are yet to be explored within this promising area of science.
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Cuevas, Erik; Sossa, Humberto; Osuna, Valentín; Zaldivar, Daniel; PérezCisneros, Marco
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Automatic circle detection in digital images has received considerable attention over the last years. Recently, several robust circle detectors, based on evolutionary algorithms (EA), have been proposed. They have demonstrated to provide better results than those based on the Hough Transform. However, since EAdetectors usually need a large number of computationally expensive fitness evaluations before a satisfying result can be obtained; their use for real time has been questioned. In this work, a new algorithm based on the Harmony Search Optimization (HSO) is proposed to reduce the number of function evaluation in the circle detection process. In order to avoid the computation of the fitness value of several circle candidates, the algorithm estimates their values by considering the fitness values from previously calculated neighboring positions. As a result, the approach can substantially reduce the number of function evaluations preserving the good search capabilities of HSO. Experimental results from several tests on synthetic and natural images with a varying complexity range have been included to validate the efficiency of the proposed technique regarding accuracy, speed and robustness.
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Oliva, Diego; Cuevas, Erik; Pajares, Gonzalo; Zaldivar, Daniel
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Template matching (TM) plays an important role in several imageprocessing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the bestpossible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the bestpossible coincidence between the images through an exhaustive computation of the normalized crosscorrelation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetismlike algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the reevaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the bestpossible resemblance is determined. The conducted simulations show that the proposed method achieves the best balance over other TM algorithms in terms of estimation accuracy and computational cost.
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Cuevas, Erik; González, Mauricio; Zaldívar, Daniel; PérezCisneros, Marco
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6 Citations
This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multimodal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact with each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions), while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed, and robustness.
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Cuevas, Erik; Zaldivar, Daniel; PérezCisneros, Marco
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20 Citations
This paper explores the use of the Learning Automata (LA) algorithm to compute threshold selection for image segmentation as it is a critical preprocessing step for image analysis, pattern recognition and computer vision. LA is a heuristic method which is able to solve complex optimization problems with interesting results in parameter estimation. Despite other techniques commonly seek through the parameter map, LA explores in the probability space providing appropriate convergence properties and robustness. The segmentation task is therefore considered as an optimization problem and the LA is used to generate the image multithreshold separation. In this approach, one 1D histogram of a given image is approximated through a Gaussian mixture model whose parameters are calculated using the LA algorithm. Each Gaussian function approximating the histogram represents a pixel class and therefore a threshold point. The method shows fast convergence avoiding the typical sensitivity to initial conditions such as the Expectation Maximization (EM) algorithm or the complex timeconsuming computations commonly found in gradient methods. Experimental results demonstrate the algorithm’s ability to perform automatic multithreshold selection and show interesting advantages as it is compared to other algorithms solving the same task.
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Cuevas, Erik; Echavarría, Alonso; RamírezOrtegón, Marte A.
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47 Citations
The ability of an Evolutionary Algorithm (EA) to find a global optimal solution depends on its capacity to find a good rate between exploitation of foundsofar elements and exploration of the search space. Inspired by natural phenomena, researchers have developed many successful evolutionary algorithms which, at original versions, define operators that mimic the way nature solves complex problems, with no actual consideration of the explorationexploitation balance. In this paper, a novel natureinspired algorithm called the States of Matter Search (SMS) is introduced. The SMS algorithm is based on the simulation of the states of matter phenomenon. In SMS, individuals emulate molecules which interact to each other by using evolutionary operations which are based on the physical principles of the thermalenergy motion mechanism. The algorithm is devised by considering each state of matter at one different exploration–exploitation ratio. The evolutionary process is divided into three phases which emulate the three states of matter: gas, liquid and solid. In each state, molecules (individuals) exhibit different movement capacities. Beginning from the gas state (pure exploration), the algorithm modifies the intensities of exploration and exploitation until the solid state (pure exploitation) is reached. As a result, the approach can substantially improve the balance between exploration–exploitation, yet preserving the good search capabilities of an evolutionary approach. To illustrate the proficiency and robustness of the proposed algorithm, it is compared to other wellknown evolutionary methods including novel variants that incorporate diversity preservation schemes. The comparison examines several standard benchmark functions which are commonly considered within the EA field. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration–exploitation balance.
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Cuevas, Erik; ReynaOrta, Adolfo; DíazCortes, MargaritaArimatea
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The main objective of multimodal optimization is to find multiple global and local optima for a problem in only one execution. Detecting multiple solutions to a multimodal optimization formulation is especially useful in engineering, since the best solution could not represent the best realizable due to various practical restrictions. The States of Matter Search (SMS) is a recently proposed stochastic optimization technique. Although SMS is highly effective in locating single global optimum, it fails in providing multiple solutions within a single execution. To overcome this inconvenience, a new multimodal optimization algorithm called the Multimodal States of Matter Search (MSMS) in introduced. Under MSMS, the original SMS is enhanced with new multimodal characteristics by means of: (1) the definition of a memory mechanism to efficiently register promising local optima according to their fitness values and the distance to other probable high quality solutions; (2) the modification of the original SMS optimization strategy to accelerate the detection of new local minima; and (3) the inclusion of a depuration procedure at the end of each state to eliminate duplicated memory elements. The performance of the proposed approach is compared to several stateoftheart multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. The results confirm that the proposed method achieves the best balance over its counterparts regarding accuracy and computational cost.
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González, Adrián; Cuevas, Erik; Fausto, Fernando; Valdivia, Arturo; Rojas, Raúl
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2 Citations
For many image processing applications (such as feature tracking, object recognition, stereo matching and remote sensing), the technique known as Template Matching (TM) plays an important role for the localization and recognition of objects or patterns within a digital image. A TM approach seeks to find a position within a source image which yields to the best possible resemblance between a given subimage (typically referred as image template) and a corresponding region of such source image. TM involves two critical aspects: similarity measurement and search strategy. In this sense, the simplest available TM method involves an exhaustive computation of the Normalized CrossCorrelation (NCC) value (similarity measurement) over all pixel locations of the source image (search strategy). Unfortunately, this approach is strongly restricted due to the high computational cost implied in the evaluation of the NCC coefficient. Recently, several TM methods based on evolutionary approaches have been proposed as an alternative to reduce the number of search locations in the TM process. However, the lack of balance between exploration and exploitation related to the operators employed by many of such approaches makes TM to suffer from several critical flaws, such as premature convergence. In the proposed approach, the swarm optimization method known as Locust Search (LS) is applied to solve the problem of template matching. The unique evolutionary operators employed by LS method’s search strategy allows to explicitly avoid the concentration of search agents toward the bestknown solutions, which in turn allows a better exploration of the valid image’s search region. Experimental results show that, in comparison to other similar methods, the proposed approach achieves the best balance between estimation accuracy and computational cost.
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Hinojosa, Salvador; Oliva, Diego; Cuevas, Erik; Pajares, Gonzalo; Avalos, Omar; Gálvez, Jorge
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This paper presents two multicriteria optimization techniques: the MultiObjective Crow Search Algorithm (MOCSA) and an improved chaotic version called MultiObjective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Crow Search Algorithm. Crows are intelligent animals with interesting strategies for protecting their food hatches. This compelling behavior is extended into a MultiObjective approach. MOCCSA uses chaoticbased criteria on the optimization process to improve the diversity of solutions. To determinate if the performance of the algorithm is significantly enhanced, the incorporation of a chaotic operator is further validated by a statistical comparison between the proposed MOCCSA and its chaoticfree counterpart (MOCSA) indicating that the results of the two algorithms are significantly different from each other. The performance of MOCCSA is evaluated by a set of standard benchmark functions, and the results are contrasted with two wellknown algorithms: MultiObjective Dragonfly Algorithm and MultiObjective Particle Swarm Optimization. Both quantitative and qualitative results show competitive results for the proposed approach.
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RamírezOrtegón, Marte A.; RamírezRamírez, Lilia L.; Messaoud, Ines Ben; Märgner, Volker; Cuevas, Erik; Rojas, Raúl
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In this article, our goal is to describe mathematically and experimentally the grayintensity distributions of the fore and background of handwritten historical documents. We propose a local pixel model to explain the observed asymmetrical grayintensity histograms of the fore and background. Our pixel model states that, locally, the grayintensity histogram is the mixture of grayintensity distributions of three pixel classes. Following our model, we empirically describe the smoothness of the background for different types of images. We show that our model has potential application in binarization. Assuming that the parameters of the grayintensity distributions are correctly estimated, we show that thresholding methods based on mixtures of lognormal distributions outperform thresholding methods based on mixtures of normal distributions. Our model is supported with experimental tests that are conducted with extracted images from DIBCO 2009 and HDIBCO 2010 benchmarks. We also report results for all four DIBCO benchmarks.
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Fausto, Fernando; Cuevas, Erik; Gonzales, Adrián
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After millions of years of evolution, nature has developed a wide variety of interesting structures, each with their own singularities and properties. Such structures provide several unique and innovative models which may be extended to solve complex engineering problems. One example of such structures is the socalled orb webs, built by many species of a spider as a part of their survival tactics. Orb webs are highly optimized structure, specifically devised to capture prey by efficiently covering a whole area with sticky threads. In this paper, a new feature descriptor called spider local image features (SLIF) is proposed. In the proposed approach, feature vectors are built by selectively extracting pictorial information from a set of previously detected interest point. This is achieved by considering a set of efficiently distributed sampling points, which emulate the intersection nodes formed by the threads of an orb web structure. The SLIF method produces simple lowdimensional feature descriptors, which are robust to several image transformation and distortions, such as scaling, rotation, bright shifts and viewpoint changes. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other wellknown feature description methods, such as those presented on the scaleinvariant feature transform, speededup robust features, binary robust scalable keypoints and fast retina keypoints. The comparison examines several different images, commonly considered as a benchmark within the image matching literature. Our experimental results evidence SLIF’s high performance and robustness against common image transformations and distortions and further show its viability for many of computer vision applications.
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Cuevas, Erik
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13 Citations
Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Blockmatching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the bestmatching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a populationbased optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.
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Cuevas, Erik; González, Mauricio
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8 Citations
Hough transform (HT) has been the most common method for circle detection that delivers robustness but adversely demands considerable computational efforts and large memory requirements. As an alternative to HTbased techniques, the problem of shape recognition has also been handled through optimization methods. In particular, extracting multiple circle primitives falls into the category of multimodal optimization as each circle represents an optimum which must be detected within the feasible solution space. However, since all optimizationbased circle detectors focus on finding only a single optimal solution, they need to be applied several times in order to extract all the primitives which results on timeconsuming algorithms. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multimodal optimization problem. In the detection, the approach employs an evolutionary algorithm based on the way in which the animals behave collectively. In such an algorithm, searcher agents emulate a group of animals which interact to each other using simple biological rules. These rules are modeled as evolutionary operators. Such operators are applied to each agent considering that the complete group maintains a memory which stores the optimal solutions seen sofar by applying a competition principle. The detector uses a combination of three noncollinear edge points as parameters to determine circle candidates (possible solutions). A matching function determines if such circle candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the evolutionary algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edgeonly image. Subsequently, an analysis of the incorporated memory is executed in order to identify potential local optima which represent other circles. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.
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Cuevas, Erik; Zaldivar, Daniel; PérezCisneros, Marco; RamírezOrtegón, Marte
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29 Citations
This paper introduces a circle detection method based on differential evolution (DE) optimization. Just as circle detection has been lately considered as a fundamental component for many computer vision algorithms, DE has evolved as a successful heuristic method for solving complex optimization problems, still keeping a simple structure and an easy implementation. It has also shown advantageous convergence properties and remarkable robustness. The detection process is considered similar to a combinational optimization problem. The algorithm uses the combination of three edge points as parameters to determine circle candidates in the scene yielding a reduction of the search space. The objective function determines if some circle candidates are actually present in the image. This paper focuses particularly on one DEbased algorithm known as the discrete differential evolution (DDE), which eventually has shown better results than the original DE in particular for solving combinatorial problems. In the DDE, suitable conversion routines are incorporated into the DE, aiming to operate from integer values to real values and then getting integer values back, following the crossover operation. The final algorithm is a fast circle detector that locates circles with subpixel accuracy even considering complicated conditions and noisy images. Experimental results on several synthetic and natural images with varying range of complexity validate the efficiency of the proposed technique considering accuracy, speed, and robustness.
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Cuevas, Erik; OsunaEnciso, Valentín; Zaldívar, Daniel; PérezCisneros, Marco
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Threshold selection is a critical step in computer vision. Immune systems, has inspired optimization algorithms known as Artificial Immune Optimization (AIO). AIO have been successfully applied to solve optimization problems. The Clonal Selection algorithm (CSA) is the most applied AIO method. It generates a response after an antigenic pattern is identified by an antibody. This works presents an image multithreshold approach based on AIS optimization. The approach considers the segmentation task as an optimization process. The 1D histogram of the image is approximated by adding several Gaussian functions whose parameters are calculated by the CSA. The mix of Gaussian functions approximates the histogram; each Gaussian function represents a pixel class (threshold point). The proposed approach is computationally efficient and does not require prior assumptions about the image. The algorithm demonstrated ability to perform automatic threshold selection.
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Gálvez, Jorge; Cuevas, Erik; Avalos, Omar; Oliva, Diego; Hinojosa, Salvador
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Evolutionary Computation Algorithms (ECA) are conceived as alternative methods for solving complex optimization problems through the search for the global optimum. Therefore, from a practical point of view, the acquisition of multiple promissory solutions is especially useful in engineering, since the global solution may not always be realizable due to several realistic constraints. Although ECAs perform well on the detection of the global solution, they are not suitable for finding multiple optima in a single execution due to their explorationexploitation operators. This paper proposes a new algorithm called Collective Electromagnetismlike Optimization (CEMO). Under CEMO, a collective animal behavior is implemented as a memory mechanism simulating natural animal dominance over the population to extend the original Electromagnetismlike Optimization algorithm (EMO) operators to efficiently register and maintain all possible Optima in an optimization problem. The performance of the proposed CEMO is compared against several multimodal schemes over a set of benchmark functions considering the evaluation of multimodal performance indexes typically found in the literature. Experimental results are statistically validated to eliminate the random effect in the obtained solutions. The proposed method exhibits higher and more consistent performance against the rest of the tested multimodal techniques.
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Cuevas, Erik; Sención, Felipe; Zaldivar, Daniel; PérezCisneros, Marco; Sossa, Humberto
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52 Citations
This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is an evolutionary algorithm inspired by the intelligent behavior of honeybees which has been successfully employed to solve complex optimization problems. In this approach, an image 1D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. In the model, each Gaussian function represents a pixel class and therefore a threshold point. Unlike the ExpectationMaximization (EM) algorithm, the ABC method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex timeconsuming computations commonly required by gradientbased methods. Experimental results over multiple images with different range of complexity validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness. The paper also includes an experimental comparison to the EM and to one gradientbased method which ultimately demonstrates a better performance from the proposed algorithm.
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Cuevas, Erik; Luque, Alberto; Zaldívar, Daniel; PérezCisneros, Marco
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Fuzzy controllers (FCs) that are based on integer schemes have demonstrated their performance in an extensive variety of applications. However, several dynamic systems can be more accurately controlled by fractional controllers yielding an increased interest in generalizing the design of FCs with fractional operators. In the design stage of fractional FCs, the parameter calibration process is transformed into a multidimensional optimization problem where fractional orders, as well as the controller parameters of the fuzzy system, are considered as decision variables. Under this approach, the complexity of the optimization problem tends to produce multimodal error surfaces for which their respective cost functions are significantly difficult to minimize. Several algorithms based on evolutionary computation principles have been successfully applied to identify the optimal parameters of fractional FCs. However, most of them still exhibit serious limitation since they frequently obtain suboptimal solutions after an improper equilibrium between exploitation and exploration in their search strategies. This paper presents an algorithm for the optimal parameter calibration of fractional FCs. In order to determine the best parameters, the proposed method uses a new evolutionary method called Social Spider Optimization (SSO), which is inspired on the emulation of the collaborative behavior of socialspiders. In SSO, solutions imitate a set of spiders, which cooperate to each other by following the natural laws of a cooperative colony. Unlike most of the existing evolutionary algorithms, the method explicitly evades the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited balance of explorationexploitation. Numerical simulations have been conducted on several plants to show the effectiveness of the proposed scheme.
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Cuevas, Erik; Díaz, Primitivo; Avalos, Omar; Zaldívar, Daniel; PérezCisneros, Marco
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The identification of realworld plants and processes, which are nonlinear in nature, represents a challenging problem. Currently, the Hammerstein model is one of the most popular nonlinear models. A Hammerstein model involves the combination of a nonlinear element and a linear dynamic system. On the other hand, the Adaptivenetworkbased fuzzy inference system (ANFIS) represents a powerful adaptive nonlinear network whose architecture can be divided into a nonlinear block and a linear system. In this paper, a nonlinear system identification method based on the Hammerstein model is introduced. In the proposed scheme, the system is modeled through the adaptation of an ANFIS scheme, taking advantage of the similarity between it and the Hammerstein model. To identify the parameters of the modeled system, the proposed approach uses a recent natureinspired method called the Gravitational Search Algorithm (GSA). Compared to most existing optimization algorithms, GSA delivers a better performance in complex multimodal problems, avoiding critical flaws such as a premature convergence to 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.
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