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By
Coello Coello, Carlos A.
Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the socalled multiobjective optimization problems). This area, known as EMOO (Evolutionary MultiObjective Optimization) has had an important growth in the last 20 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this paper, we will briefly review some of the research topics on evolutionary multiobjective optimization that are currently attracting a lot of interest (e.g., indicatorbased selection, manyobjective optimization and use of surrogates) and which represent good opportunities for doing research. Some of the challenges currently faced by this discipline will also be delineated.
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By
Cruz Reyes, Laura; Delgado Orta, José F.; González Barbosa, Juan J.; Torres Jimenez, José; Fraire Huacuja, Héctor J.; Arrañaga Cruz, Bárbara A.
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7 Citations
This work presents a methodology of solution for the wellknown vehicle routing problem (VRP) based on an ant colony system heuristic algorithm (ACS), which is applied to optimize the delivery process of RoSLoP (RoutingSchedulingLoading Problem) identified in the company case of study. A first version of this algorithm models six variants of VRP and its solution satisfies the 100% of demands of the customers. The new version of the algorithm can solve 11 variants of VRP as a rich VRP. Experiments were carried out with real instances. The new algorithm shows a saving of two vehicles with regard to the first version, reducing the operation costs of the company. These results prove the viability of using heuristic methods and optimization techniques to develop new software applications.
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By
Flores, Juan J.; López, Rodrigo; Barrera, Julio
3 Citations
Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a populationbased metaheuristic inspired on the gravitational forces produced by the interaction of the masses of a set of bodies. We explored the physics knowledge in order to find useful analogies to design an optimization metaheuristic. The proposed algorithm is capable to find the optima of unimodal and multimodal functions commonly used to benchmark evolutionary algorithms. We show that the proposed algorithm (Gravitational Interactions Optimization  GIO) works and outperforms PSO with niches in both cases. Our algorithm does not depend on a radius parameter and does not need to use niches to solve multimodal problems. We compare GIO with other metaheuristics with respect to the mean number of evaluations needed to find the optima.
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By
FraustoSolís, Juan; SanvicenteSánchez, Héctor; ImperialValenzuela, Froilán
5 Citations
Because the efficiency and efficacy in Simulated Annealing (SA) algorithms is determined by their cooling scheme, several methods to set it have been proposed. In this paper an analytical method (ANDYMARK) to tune the parameters of the cooling scheme in SA for the Satisfiability (SAT) problem is presented. This method is based on a relation between the Markov chain’s length and the cooling scheme. We compared ANDYMARK versus a classical SA algorithm that uses the same constant Markov chain. Experimentation with SAT instances shows that SA using this method obtains similar quality solutions with less effort than the classical one.
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By
HervertEscobar, Laura; Alexandrov, Vassil
A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design a sales territory for a business sales plan. The business plan consists in assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization problems. The solution approach considers a metaheuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers.
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By
Reyes, Laura Cruz; Zezzatti, Carlos Alberto Ochoa Ortíz; Santillán, Claudia Gómez; Hernández, Paula Hernández; Fuerte, Mercedes Villa
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8 Citations
In the last years the population of Leon City, located in the state of Guanajuato in Mexico, has been considerably increasing, causing the inhabitants to waste most of their time with public transportation. As a consequence of the demographic growth and traffic bottleneck, users deal with the daily problem of optimizing their travel so that to get to their destination on time. To give a solution to this problem of obtaining an optimized route between two points in a public transportation, a method based on the cultural algorithms technique is proposed. Cultural algorithms are used in the generated knowledge in a set of time periods for a same population, using a belief space. These types of algorithms are a recent creation. The proposed method seeks a path that minimizes the time of traveling and the number of transfers. The results of the experiment show that the technique of the cultural algorithms is applicable to these kinds of multiobjective problems.
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By
LópezCamacho, Eunice; TerashimaMarín, Hugo; Ross, Peter
2 Citations
In various approaches for combinatorial optimization, a problem instance is represented by a numerical vector that summarizes to some extent the actual solution state of such instance. Such representation intends to include the most relevant features related to the instance and the problem domain. The proper selection of these features has a direct impact on the performance of a hyperheuristic. Previous approaches for hyperheuristics have been relying on intuitive ways to determine the feature set, usually based on the domain knowledge of a particular problem. In this paper, a more general methodology for establishing an adequate problemstate representation is proposed. We chose the irregular case of the twodimensional Bin Packing Problem (2D irregular BPP) and a GAbased hyperheuristic model to test the methodology. As far as we know, this is the only hyperheuristic model applied to the 2D irregular BPP and it has been successful when solving a wide range of instances. Our developed representation shows a significant improvement in performance with respect to a more conventional representation for the 2D irregular BPP.
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By
TorresJimenez, Jose; AvilaGeorge, Himer; RangelValdez, Nelson; MartinezPena, Jorge
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This paper presents a novel use of SQL language to solve a practical optimization problem to find the portfolio size and the quantity of money for securities. This problem is known as the Portfolio Selection Problem (PSP). The approach was tested on 9 random instances of PSP. Each instance has up to 12 securities and 50 different choices of money. Each security follows a nonlinear profit model. The limitations of our approach are bounded by the computer resources, given that potentially SQL constructs the Cartesian product of the investment options, but it has the advantages of not requiring complex structures and it is easy to understand.
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By
Chairez, Isaac; Poznyak, Alexander; Nazin, Alexander; Poznyak, Tatyana
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A new method to design learning laws for neural networks with continuous dynamics is proposed in this study. The learning method is based on the socalled doubleaveraged descendant technique (DASGDT), which is a variant of the gradientdescendant method. The learning law implements a double averaged algorithm which filters the effect of uncertainties of the states, which are continuously measurable. The learning law overcomes the classical assumption on the strict convexity of the functional with respect to the weights. The photocatalytic ozonation process of a single contaminant is estimated using the learning law design proposed in this study.
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By
RodriguezMaya, Noel; Flores, Juan J.; Graff, Mario
The University Course Timetabling Problem (UCTP) is a well known optimization problem. Literature reports different methods and techniques to solve it, being Evolutionary Algorithms (EA) one of the most successful. In the EA field, the selection of the best algorithm and its parameters to solve a particular problem, is a difficult problem; would be helpful to know a priori the performance related to that algorithm. Fitness Landscape Analysis (FLA) is a set of tools to describe optimization problems and for the prediction of the performance related with EA. FLA uses a set of metrics to characterize the landscape depicted by a cost function, aiming to understand the behaviour of search algorithms. This work presents an empirical study to characterize some instances of UCTP, and for the prediction of difficulty exhibited by RealCoded Genetic Algorithms (RCGA) to solve the instances. We used FLA as characterization schema; neutrality, ruggedness, and negative slope coefficient are the metrics used in the analysis. To test and validate the proposal, we use three UCTP instances based on Mexican universities. Incipient results suggest an correlation between the negative slope coefficient and the difficulty exhibited by RCGA in the solution of UCTP instances. Ruggedness and neutrality provide the global structure of the instances’s landscape.
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By
Segovia Domínguez, Ignacio; Hernández Aguirre, Arturo; Villa Diharce, Enrique
This paper introduces the Gaussian polytree estimation of distribution algorithm, a new construction method, and its application to estimation of distribution algorithms in continuous variables. The variables are assumed to be Gaussian. The construction of the tree and the edges orientation algorithm are based on information theoretic concepts such as mutual information and conditional mutual information. The proposed Gaussian polytree estimation of distribution algorithm is applied to a set of benchmark functions. The experimental results show that the approach is robust, comparisons are provided.
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By
Lezama, Fernando; Palominos, Jorge; RodríguezGonzález, Ansel Y.; Farinelli, Alessandro; Cote, Enrique Munoz
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The current energy scenario requires actions towards the reduction of energy consumptions and the use of renewable resources. To this end, the energy grid is evolving towards a distributed architecture called Smart Grid (SG). Moreover, new communication paradigms, such as the Internet of Things (IoT), are being applied to the SG providing advanced communication capabilities for management and control. In this context, a microgrid is a selfsustained network that can operate connected to the SG (or in isolation). In such networks, the longterm scheduling of on/off cycles of devices is a problem that has been commonly addressed by centralized approaches. In this paper, we propose a novel IoTmicrogrid architecture to model the longterm optimization scheduling problem as a distributed constraint optimization problem (DCOP). We compare different multiagent DCOP algorithms using different window sizes showing that the proposed architecture can find optimal and nearoptimal solutions for a specific case study.
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By
RechyRamírez, Fernando; Mesa, HéctorGabriel Acosta; MezuraMontes, Efrén; CruzRamírez, Nicandro
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3 Citations
In this work, we present a novel algorithm for time series discretization. Our approach includes the optimization of the word size and the alphabet as one parameter. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. Our proposal is compared with some of the most representative algorithms found in the specialized literature, tested in a wellknown benchmark of time series data sets. The statistical analysis of the classification accuracy shows that the overall performance of our algorithm is highly competitive.
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By
Fuentes, Olac; Solorio, Thamar
We present an optimization algorithm that combines active learning and locallyweighted regression to find extreme points of noisy and complex functions. We apply our algorithm to the problem of interferogram analysis, an important problem in optical engineering that is not solvable using traditional optimization schemes and that has received recent attention in the research community. Experimental results show that our method is faster than others previously presented in the literature and that it is very accurate for the case of noiseless interferograms, as well as for the case of interferograms with two types of noise: white noise and intensity gradients, which are due to slight missalignments in the system.
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By
Flores, Juan J.; López, Rodrigo; Barrera, Julio
2 Citations
Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a populationbased metaheuristic inspired on the gravitational forces produced by the interaction of the masses of a set of bodies. We explored the physics knowledge in order to find useful analogies to design an optimization metaheuristic. The proposed algorithm is capable to find the optima of unimodal and multimodal functions commonly used to benchmark evolutionary algorithms. We show that the proposed algorithm works and outperforms PSO with niches in both cases. Our algorithm does not depend on a radius parameter and does not need to use niches to solve multimodal problems. We compare with other metaheuristics respect to the mean number of evaluations needed to find the optima.
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By
HerbertAcero, JoséFrancisco; FrancoAcevedo, JorgeRodolfo; ValenzuelaRendón, Manuel; ProbstOleszewski, Oliver
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3 Citations
The optimal positioning of wind turbines, even in one dimension, is a problem with no analytical solution. This article describes the application of computational intelligence techniques to solve this problem. A systematic analysis of the optimal positioning of wind turbines on a straight line, on flat terrain, and considering wake effects has been conducted using both simulated annealing and genetic algorithms. Free parameters were the number of wind turbines, the distances between wind turbines and wind turbine hub heights. Climate and terrain characteristics were varied, like incoming wind speed, wind direction, air density, and surface roughness length, producing different patterns of positioning. Analytical functions were used to model wake effects quantifying the reduction in speed after the wind passes through a wind turbine. Conclusions relevant to the placement of wind turbines for several cases are presented.
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By
Almeida Arrieta, Betrand J.; AlvaradoNava, Oscar; Chablé Martínez, Hilda M.; RodríguezMartínez, Eduardo; Zaragoza Martínez, Francisco Javier
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1 Citations
We describe the parallel implementation of an evolutionary programming algorithm for minimization of nonlinear, continuous, realvalued functions of n variables. The parallel implementation was carried using the GPGPU (GeneralPurpose Computing on Graphics Processing Units) technique. Evolutionary programming (EP) was selected from the available evolutionary algorithm paradigms because it presents low dependency between its genetic operators. This feature provided a particular advantage to parallelize the mutation and evaluation stages in EP using a masterslave model. The obtained results report a linear speed up with respect to the number of cores in the test platform.
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By
Diana, SánchezPartida; Karen, Zamudio; SantiagoOmar, CaballeroMorales; Luis, MartínezFlores José
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For all organizations in the manufacturing and service sectors, transportation has become an important topic for the economic decisionmaking process. This is due to the high quality and versatility standards that today the industries have established to follow a strict guideline focused on JustInTime (JIT). In this context, the present paper extends on the analysis and improvement of a realworld routing problem of a Mexican company dedicated to the manufacturing of plastic products. The value chain of the company has its origin at the gathering (pickingup) of raw materials and components from different suppliers located in the United States. Currently, a thirdparty company (carrier) has been hired to perform the pickup process, however there is uncertainty regarding the optimality of its routes and their associated costs. The present research analyses the current routing scheme that is performed by the carrier to verify compliance of the requirements of the company which are minimization of costs and travel times. Then, the Capacitated Vehicle Routing Problem (CVRP) is performed for improvement of the current routing scheme. In addition, a new location for a depot (or collection center) is proposed to allow the company to manage its own raw material, avoiding the need to consider a thirdparty company for the pickingup services by means of the pMedian Problem (PMP). Optimization for the CVRP and PMP was performed with a specialized software and the results obtained by this research present an improved costefficient routing scheme for the pickingup process of the plastic company.
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By
Cuevas, Erik; González, Mauricio; Zaldívar, Daniel; PérezCisneros, Marco
Show all (4)
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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By
Cruz, Benjamín; Barrón, Ricardo; Sossa, Humberto
1 Citations
Conformal Geometric Algebra (CGA) is a high level language commonly used in mathematical, physics and engineering problems. At a top level, CGA is a free coordinate tool for designing and modeling geometric problems; at a low level CGA provides a new coordinate framework for numeric processing in problem solving. In this paper we show how to use quadratic programming and CGA for, given two sets p and q of points in ℝ^{n}, construct an optimal separation sphere S such that, all points of p are contained inside of it, and all points of q are outside. To classify an unknown pattern x, an inner product must be applied between x and S. Some numerical and real examples to test the proposal are given.
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By
González, Beatriz; Valdez, Fevrier; Melin, Patricia
In this paper we are presenting a modification of the Gravitational Search Algorithm (GSA) using type2 fuzzy logic to dynamically change the alpha parameter and provide a different gravitation and acceleration values to each agent in order to improve its performance. We test this approach with benchmark mathematical functions. Simulation results show the advantages of the proposed approach.
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By
FernandezRamirez, Karla I.; Baltazar, Arturo
In this work, the delay and sum (DAS) beamforming algorithm commonly used in several areas of engineering and robotics is modified and implemented for the identification and localization of acoustic sources. Its classical approach uses a systematic scanning of all points in a given space domain to localize a disturbance source. DAS is efficient when the searching area is small, but it becomes time consuming when the area increases, or when its topology is unknown. Here, an algorithm that uses beamforming information and a chaotic search scheme for optimal target localization is proposed. The algorithm is performed in two stages: first, the entire work area is mapped using chaotic sequences to determine a vector with the locations with a high probability of finding a source. The second stage initiates a search for a global optimum using chaotic walk trajectories. The proposed algorithm is tested with known analytical functions and then implemented using a time domain simulation of acoustic field and an array of sensors. The algorithm performance for the synthetic signals was compared with the traditional systematic scan. The results showed a reduction in searching time of 90% with similar localization accuracy as the typical beamforming.
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By
Méndez, Eduardo; Castillo, Oscar; Soria, José; Melin, Patricia; Sadollah, Ali
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1 Citations
This paper describes the enhancement of the Water Cycle Algorithm (WCA) using a fuzzy inference system to adapt its parameters dynamically. The original WCA is compared regarding performance with the proposed method called Water Cycle Algorithm with Dynamic Parameter Adaptation (WCADPA). Simulation results on a set of wellknown test functions show that the WCA can be improved with a fuzzy dynamic adaptation of the parameters.
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By
IbarraMartínez, Salvador; CastánRocha, José A.; LariaMenchaca, Julio
This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a casebased reasoning (CBR) adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.
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By
Hernandez, Leonel; Balmaceda, Nidia; Hernandez, Hugo; Vargas, Carlos; Hoz, Emiro; Orellano, Nataly; Vasquez, Emilse; UcRios, Carlos Eduardo
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The campus wireless networks have many users, who have different roles and network requirements, ranging from the use of educational platforms, informative consultations, emails, among others. Currently due to the inefficient use of network resources and little wireless planning, caused by the growth of the technological infrastructure (which is often due to daily worries, rather than to a lack of preparation by those in charge of managing the network), There are two essential factors that truncate the requirement of having a stable and robust network platform. First, the degradation of the quality of services perceived by users, and second, the congestion caused by the high demand for convergent traffic (video, voice, and data). Both factors imply great challenges on the part of the administrators of the network, which in many occasions are overwhelmed by permanent incidences of instability, coverage, and congestion, as well as the difficulty of maintaining it economically. The present investigation seeks to propose a process of optimization of the infrastructure and parameters of the configuration of a wireless network, that allows maximizing the level of satisfaction of the users in Higher Education Institutions. In the first place, it is expected to determine an adequate methodology to estimate the level of satisfaction of the users (defining a mathematical criterion or algorithm based on the study variables [1], characterize the environment in which the project will be developed, making a complete study of the wireless conditions and implement optimization strategies with softwaredefined networks (SDN). SDN is a concept in computer networks that allows network management to be carried out efficiently and flexibly, separating the control plane from the data plane into network devices. SDN architecture consists of an infrastructure layer which is a collection of network devices connected to the SDN Controller using protocol (OpenFlow) as a protocol [2]. Also, SDN will study traffic patterns on the network as a basis for optimizing network device usage [3]. The phases of the research will be carried out following the life cycle defined by the Cisco PPDIOO methodology (Prepare, Plan, Design, Implement, Operate, Optimize) [4].
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By
HervertEscobar, Laura; LópezPérez, Jesus Fabian; EsquivelFlores, Oscar Alejandro
Pricing is one of the most vital and highly demanded component in the mix of marketing along with the Product, Place and Promotion. An organization can adopt a number of pricing strategies, which usually will be based on corporate objectives. The purpose of this paper is to propose a methodology to define an optimal pricing strategy for convenience stores. The solution approach involves a multiple linear regression as well as a linear programming optimization model. To prove the value of the proposed methodology a pilot was performed for selected stores. Results show the value of the solution methodology. This model provides an innovative solution that allows the decision maker include business rules of their particular environment in order to define a price strategy that meet the objective business goals.
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By
LopezMartinez, A.; Cuevas, F. J.
Circle extraction is usually a previous task used in different applications related to biometrics, robotics, medical image analysis among others. Solutions based on metaheuristic approaches, such as evolutionary and swarmbased algorithms, have been adopted in order to overcome the main deficiencies of Hough Transform methods. In this paper, the task of circle detection is presented as an optimization problem, where each circle represents an optimum within the feasible search space. To this end, a circle detection method is proposed based on the Teaching Learning Based Optimization algorithm, which is a populationbased technique that is inspired by the teaching and learning processes. Additionally, improvements to the evolutionary approach for circle detection are obtained by exploiting gradient information for the construction of the search space and the definition of the objective function. To validate the efficacy of the proposed circle detector, several tests using noisy and complex images as input were carried out, and the results compared with different approaches for circle detection.
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By
Melin, Patricia; Pulido, Martha; Castillo, Oscar
1 Citations
This paper describes the design of ensemble neural networks using Particle Swarm Optimization (PSO) for time series prediction with Type1 and Type2 Fuzzy Integration. The time series that is being considered in this work is the MackeyGlass benchmark time series. Simulation results show that the ensemble approach produces good prediction of the MackeyGlass time series.
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By
Hernández Carreón, Carlos A.; Fraire Huacuja, Héctor J.; Fernandez, Karla Espriella; Valdez, Guadalupe Castilla; Mancilla Tolama, Juana E.
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1 Citations
This paper presents an application of a genetic algorithm (GA) to the scheduling of hot rolling mills. The objective function used is based on earlier developments on flow stress modeling of steels. A hybrid twophase procedure was applied in order to calculate the optimal pass reductions, in terms of minimum total rolling time. In the first phase, a nonlinear optimization function was applied to evaluate the computational cost to the problem solution. For the second phase, a GA was applied. A comparison with twopoint and simulated binary (SBX) crossover operators was established. The results were validated with data of industrial schedules. A GA with SBX crossover operator is shown to be an efficient method to calculate the multipass schedules at reduced processing time.
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By
Cuevas, Erik; González, Mauricio
9 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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By
Treesatayapun, Chidentree
1 Citations
In this article, an adaptive controller, which can minimize both tracking error and control energy, is introduced by fuzzy rule emulated network (FREN) for a class of nonaffine discrete time systems. The controlled plant can be assumed as fully unknown system dynamic. Only the estimated boundary of pseudo partial derivative (PPD) is required for an online learning phase. The update law is derived to guarantee the convergence of tuned parameters. Lyapunov techniques are utilized to demonstrate the performance of a closedloop system regarding the integration of the infinite cost function. The computer simulation and electronic circuit system validate the effectiveness of the proposed control scheme.
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By
Iturriaga, Santiago; Goñi, Gerardo; Nesmachnow, Sergio; Dorronsoro, Bernabé; Tchernykh, Andrei
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This work addresses the multiobjective resource provisioning problem for building cloudbased CDNs. The optimization objectives are the minimization of VM, network and storage cost, and the maximization of the QoS for the enduser. A brokering model is proposed such that a single cloudbased CDN is able to host multiple content providers applying a resource sharing strategy. Following this model, an offline multiobjective evolutionary approach is applied to optimize resource provisioning while a greedy heuristic is proposed for addressing online routing of content. Experimental results indicate the proposed approach may reduce total costs by up to 10.6% while maintaining high QoS values.
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