Alejo-Reyes, Avelina
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Alejo-Reyes, Avelina
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aalejo
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57202425879
Researcher ID
EKT-0166-2022

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An Evolutionary Algorithm-Based PWM Strategy for a Hybrid Power Converter

2020 , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Erik Cuevas , Francisco Beltran-Carbajal , Rosas-caro, Julio

In the past years, the interest in direct current to direct current converters has increased because of their application in renewable energy systems. Consequently, the research community is working on improving its efficiency in providing the required voltage to electronic devices with the lowest input current ripple. Recently, a hybrid converter which combines the boost and the Cuk converter in an interleaved manner has been introduced. The converter has the advantage of providing a relatively low input current ripple by a former strategy. However, it has been proposed to operate with dependent duty cycles, limiting its capacity to further decrease the input current ripple. Independent duty cycles can significantly reduce the input current ripple if the same voltage gain is achieved by an appropriate duty cycle combination. Nevertheless, finding the optimal duty cycle combination is not an easy task. Therefore, this article proposes a new pulse-width-modulation strategy for the hybrid interleaved boost-Cuk converter. The strategy includes the development of a novel mathematical model to describe the relationship between independent duty cycles and the input current ripple. The model is introduced to minimize the input current ripple by finding the optimal duty cycle combination using the differential evolution algorithm. It is shown that the proposed method further reduces the input current ripple for an operating range. Compared to the former strategy, the proposed method provides a more balanced power-sharing among converters.

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Blood Vessel Segmentation Using Differential Evolution Algorithm

2021 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Del-Valle-Soto, Carolina

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Metaheuristic Algorithm Based on Hybridization of Invasive Weed Optimization asnd Estimation Distribution Methods

2021 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Del-Valle-Soto, Carolina

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Corner Detection Algorithm Based on Cellular Neural Networks (CNN) and Differential Evolution (DE)

2021 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Del-Valle-Soto, Carolina

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A Mathematical Model for an Inventory Management and Order Quantity Allocation Problem with Nonlinear Quantity Discounts and Nonlinear Price-Dependent Demand

2023 , Alejo-Reyes, Avelina , Mendoza, Abraham , Erik Cuevas , Alcaraz Rivera, Miguel

This article focuses on solving the order quantity allocation problem for retailers. It considers factors such as quality constraints, nonlinear quantity discounts, and price-dependent demand. By formulating it as a nonlinear maximization problem, the article aims to find the best combination of suppliers and order quantity out of infinite solutions to maximize the retailer’s profit. The main contribution of this research is a new mathematical model that can solve the problem of quality constraint and demand in a single step. This problem is complex due to the number of equations, their nonlinear nature, and the various trade-offs given by the market. Additionally, this research considers demand as output and includes price-dependent demand, which is more realistic for retailers. The proposed model was tested using an example from the recent literature and showed better results than the previously published best solution regarding profit maximization.

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Metaheuristic Algorithms Applied to the Inventory Problem

2021 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Del-Valle-Soto, Carolina

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An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

2020 , Alejo-Reyes, Avelina , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Mendoza, Abraham , Olivares-Benitez, Elias

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

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A Modified Simulated Annealing (MSA) Algorithm to Solve the Supplier Selection and Order Quantity Allocation Problem with Non-Linear Freight Rates

2023 , González Cabrera, Nora Paulina , Alejo-Reyes, Avelina , Erik Cuevas , Mendoza, Abraham

Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time.

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Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes

2023 , Erik Cuevas , Rodríguez Vázquez, Alma Nayeli , Marco Perez , Jesús Murillo-Olmos , Bernardo Morales-Castañeda , Ram Sarkar , Alejo-Reyes, Avelina

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Numerical Optimization of Switching Ripples in the Double Dual Boost Converter through the Evolutionary Algorithm L-SHADE

2020 , Rodríguez Vázquez, Alma Nayeli , Alejo-Reyes, Avelina , Erik Cuevas , Robles-Campos, Héctor R. , Rosas-caro, Julio

Power-electronics based converters are essential circuits in renewable energy applications such as electricity generated with photovoltaic panels. The research on the field is getting increasing attention due to climate change problems and their possible attenuation with the use of renewable energy. Mathematical models of the converters are being used to optimize several aspects of their operation. This article is dedicated to optimizing (through the mathematical model and an evolutionary algorithm) the operation of a state-of-the-art converter. The converter, which is composed of two parts or phases, is controlled by pulse width modulation with two switching signals (one for each phase). The converter provides by itself low switching ripple in both the output voltage and the input current, which is beneficial for renewable energy applications. In the traditional operation, one of the switching signals has an algebraic dependence on the other one. This article proposes a new way to select the duty cycle for both signals. In the proposed method, duty cycles of both phases are considered independent of each other; this provides an extra degree of freedom; on the other hand, this produce that the possible combinations of duty cycles which produce a certain voltage gain is infinite, it becomes a problem with infinite possible solutions. The proposed method utilizes the a linear success-history based adaptive differential evolution with linear population reduction, also called L-SHADE algorithm for simplicity, to find the two duty cycles that achieve the desired voltage gain and to minimize the converters switching ripple. The obtained results are compared with the former operation of the converter; the proposed operation achieves a lower output voltage ripple while achieving the desired operation (voltage gain).

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