Special Issue "Numerical and Evolutionary Optimization 2019"

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Prof. Dr. America Morales
Website
Guest Editor
Robotics and Advanced Manufacturing Program, CINVESTAV-IPN, 25900 Saltillo, Mexico
Interests: nonlinear control systems with applications to process and mobile robots cooperation
Prof. Dr. Mario Castelán
Website
Guest Editor
Robotics and Advanced Manufacturing Program, CINVESTAV-IPN, 25900 Saltillo, Mexico
Interests: computer vision; statistical methods for recovery and shape recognition; facial analysis; shape-from-X techniques; autonomous vehicles; robot localization; deep learning
Dr. Marcela Quiroz

Guest Editor
Centro de Investigación en Inteligencia Artificial, University of Veracruz, 91000 Xalapa, Mexico
Interests: experimental algorithmics; metaheuristics; genetic algorithms; bin packing; machine learning; causal inference applications
Special Issues and Collections in MDPI journals
Prof. Dr. Oliver Schütze
Website
Guest Editor
Depto de Computacion, CINVESTAV-IPN, 07360 Mexico City, Mexico
Interests: multi-objective optimization; evolutionary computation (genetic algorithms and evolution strategies); numerical analysis; engineering applications
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of selected papers presented at the 7th International Workshop on Numerical and Evolutionary Optimization (NEO 2019, see http://neo.cinvestav.mx for detailed information). However, other works that fit within the scope of the NEO are welcome. Papers considered to fit the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge.

The aim of this Special Issue is to collect papers on the intersection of numerical and evolutionary optimization. We strongly encourage the development of fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of each underlying paradigm while also being applicable to a broader class of problems. Moreover, this Special Issue aims to foster the understanding and adequate treatment of real-world problems, particularly in emerging fields that affect us all, such as healthcare, smart cities, and big data, among many others.

Topics of interest include (but are not limited to) the following:

(A) Robotics:

  • Mobile robotics: wheels, drones, mobile manipulators, aquatic robots;
  • Cooperative robots: manipulators, multivehicle systems indoors and outdoors;
  • Service robots;
  • Robots for medical applications;
  • Robots in agricultural applications;
  • Techniques to design robots;
  • Control and planning algorithms.

(B) Search and optimization:

  • Single- and multi-objective optimization;
  • Advances in evolutionary algorithms and genetic programming;
  • Hybrid and memetic algorithms;
  • Set oriented numerics;
  • Stochastic optimization;
  • Robust optimization.

(C) Real-world problems:

  • Health systems;
  • Computer vision and pattern recognition;
  • Energy conservation and prediction;
  • Modeling and control of real-world systems;
  • Smart cities.

Prof. Dr. America Morales
Prof. Dr. Mario Castelán
Dr. Marcela Quiroz
Prof. Dr. Oliver Schütze
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematical and Computational Applications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

Open AccessArticle
Proximal Gradient Method for Solving Bilevel Optimization Problems
Math. Comput. Appl. 2020, 25(4), 66; https://doi.org/10.3390/mca25040066 - 04 Oct 2020
Abstract
In this paper, we consider a bilevel optimization problem as a task of finding the optimum of the upper-level problem subject to the solution set of the split feasibility problem of fixed point problems and optimization problems. Based on proximal and gradient methods, [...] Read more.
In this paper, we consider a bilevel optimization problem as a task of finding the optimum of the upper-level problem subject to the solution set of the split feasibility problem of fixed point problems and optimization problems. Based on proximal and gradient methods, we propose a strongly convergent iterative algorithm with an inertia effect solving the bilevel optimization problem under our consideration. Furthermore, we present a numerical example of our algorithm to illustrate its applicability. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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Open AccessArticle
Nonlinear Analysis for a Type-1 Diabetes Model with Focus on T-Cells and Pancreatic β-Cells Behavior
Math. Comput. Appl. 2020, 25(2), 23; https://doi.org/10.3390/mca25020023 - 24 Apr 2020
Abstract
Type-1 diabetes mellitus (T1DM) is an autoimmune disease that has an impact on mortality due to the destruction of insulin-producing pancreatic β -cells in the islets of Langerhans. Over the past few years, the interest in analyzing this type of disease, either in [...] Read more.
Type-1 diabetes mellitus (T1DM) is an autoimmune disease that has an impact on mortality due to the destruction of insulin-producing pancreatic β -cells in the islets of Langerhans. Over the past few years, the interest in analyzing this type of disease, either in a biological or mathematical sense, has relied on the search for a treatment that guarantees full control of glucose levels. Mathematical models inspired by natural phenomena, are proposed under the prey–predator scheme. T1DM fits in this scheme due to the complicated relationship between pancreatic β -cell population growth and leukocyte population growth via the immune response. In this scenario, β -cells represent the prey, and leukocytes the predator. This paper studies the global dynamics of T1DM reported by Magombedze et al. in 2010. This model describes the interaction of resting macrophages, activated macrophages, antigen cells, autolytic T-cells, and β -cells. Therefore, the localization of compact invariant sets is applied to provide a bounded positive invariant domain in which one can ensure that once the dynamics of the T1DM enter into this domain, they will remain bounded with a maximum and minimum value. Furthermore, we analyzed this model in a closed-loop scenario based on nonlinear control theory, and proposed bases for possible control inputs, complementing the model with them. These entries are based on the existing relationship between cell–cell interaction and the role that they play in the unchaining of a diabetic condition. The closed-loop analysis aims to give a deeper understanding of the impact of autolytic T-cells and the nature of the β -cell population interaction with the innate immune system response. This analysis strengthens the proposal, providing a system free of this illness—that is, a condition wherein the pancreatic β -cell population holds and there are no antigen cells labeled by the activated macrophages. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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Open AccessArticle
A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm
Math. Comput. Appl. 2020, 25(1), 17; https://doi.org/10.3390/mca25010017 - 19 Mar 2020
Cited by 2
Abstract
One of the effective ways of reducing power system losses is local compensation of part of the reactive power consumption by deploying shunt capacitor banks. Since the capacitor’s impedance is frequency-dependent and it is possible to generate resonances at harmonic frequencies, it is [...] Read more.
One of the effective ways of reducing power system losses is local compensation of part of the reactive power consumption by deploying shunt capacitor banks. Since the capacitor’s impedance is frequency-dependent and it is possible to generate resonances at harmonic frequencies, it is important to provide an efficient method for the placement of capacitor banks in the presence of nonlinear loads which are the main cause of harmonic generation. This paper proposes a solution for a multi-objective optimization problem to address the optimal placement of capacitor banks in the presence of nonlinear loads, and it establishes a reasonable reconciliation between costs, along with improvement of harmonic distortion and a voltage index. In this paper, while using the harmonic power flow method to calculate the electrical quantities of the grid in terms of harmonic effects, the non-dominated sorting genetic (NSGA)-II multi-objective genetic optimization algorithm was used to obtain a set of solutions named the Pareto front for the problem. To evaluate the effectiveness of the proposed method, the problem was tested for an IEEE 18-bus system. The results were compared with the methods used in eight other studies. The simulation results show the considerable efficiency and superiority of the proposed flexible method over other methods. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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Open AccessArticle
Chaos Synchronization for Hyperchaotic Lorenz-Type System via Fuzzy-Based Sliding-Mode Observer
Math. Comput. Appl. 2020, 25(1), 16; https://doi.org/10.3390/mca25010016 - 14 Mar 2020
Abstract
Hyperchaotic systems have applications in multiple areas of science and engineering. The study and development of these type of systems helps to solve diverse problems related to encryption and decryption of information. In order to solve the chaos synchronization problem for a hyperchaotic [...] Read more.
Hyperchaotic systems have applications in multiple areas of science and engineering. The study and development of these type of systems helps to solve diverse problems related to encryption and decryption of information. In order to solve the chaos synchronization problem for a hyperchaotic Lorenz-type system, we propose an observer based synchronization under a master-slave configuration. The proposed methodology consists of designing a sliding-mode observer (SMO) for the hyperchaotic system. In contrast, this type of methodology exhibits high-frequency oscillations, commonly known as chattering. To solve this problem, a fuzzy-based SMO system was designed. Numerical simulations illustrate the effectiveness of the synchronization between the hyperchaotic system obtained and the proposed observer. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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Open AccessArticle
Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization
Math. Comput. Appl. 2020, 25(1), 7; https://doi.org/10.3390/mca25010007 - 16 Jan 2020
Cited by 2
Abstract
Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic [...] Read more.
Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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Open AccessFeature PaperArticle
Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions
Math. Comput. Appl. 2020, 25(1), 3; https://doi.org/10.3390/mca25010003 - 08 Jan 2020
Cited by 2
Abstract
In this paper, we present a novel evolutionary algorithm for the computation of approximate solutions for multi-objective optimization problems. These solutions are of particular interest to the decision-maker as backup solutions since they can provide solutions with similar quality but in different regions [...] Read more.
In this paper, we present a novel evolutionary algorithm for the computation of approximate solutions for multi-objective optimization problems. These solutions are of particular interest to the decision-maker as backup solutions since they can provide solutions with similar quality but in different regions of the decision space. The novel algorithm uses a subpopulation approach to put pressure towards the Pareto front while exploring promissory areas for approximate solutions. Furthermore, the algorithm uses an external archiver to maintain a suitable representation in both decision and objective space. The novel algorithm is capable of computing an approximation of the set of interest with good quality in terms of the averaged Hausdorff distance. We underline the statements on some academic problems from literature and an application in non-uniform beams. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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Open AccessFeature PaperArticle
Optimizing the Maximal Perturbation in Point Sets while Preserving the Order Type
Math. Comput. Appl. 2019, 24(4), 97; https://doi.org/10.3390/mca24040097 - 16 Nov 2019
Abstract
Recently a new kind of fiducial marker based on order type (OT) has been proposed. Using OT one can unequivocally identify a set of points through its triples of point orientation, and therefore, there is no need to use metric information. These proposed [...] Read more.
Recently a new kind of fiducial marker based on order type (OT) has been proposed. Using OT one can unequivocally identify a set of points through its triples of point orientation, and therefore, there is no need to use metric information. These proposed order type tags (OTTs) are invariant under a projective transformation which allows identification of them directly from a photograph. The magnitude of noise in the point positions that a set of points can support without changing its OT, is named the maximal perturbation (MP) value. This value represents the maximal displacement that any point in the set can have in any direction without changing the triplet’s orientation in the set. A higher value of the MP makes an OTT instance more robust to perturbations in the points positions. In this paper, we address the problem of how to improve the MP value for sets of points. We optimize “by hand” the MP for all the 16 subsets of points in the set of OTs composed of six points, and we also propose a general algorithm to optimize all the sets of OTs composed of six, seven, and eight points. Finally, we show several OTTs with improved MP values, and their use in an augmented reality application. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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