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Advances in Intelligent System for Control and Complex Optimization Problems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 24693

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Prof. Dr. Ignacio Rojas

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Information Technology and Telecommunications Engineering, University of Granada, 18010 Granada, Spain
Interests: time series analysis; forecasting; artificial intelligence; biomedical engineering; complex problems
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Special Issue Information

Dear Colleagues,

New advances in artificial intelligence (AI), intelligent systems, soft computing and related scientific fields have provided new opportunities and challenges for researchers in tackling complex and uncertain problems and systems that could not be solved by traditional methods. In the field of control and complex optimization problems, this is one of the most important challenges today. Many traditional methods have been developed for mathematically well-defined problems with exact models. However, in real, complex problems, obtaining accurate models is difficult, taking into account the possible causes of noise and disturbances that may occur.

Intelligent systems are defined by attributes such as a high degree of autonomy, reasoning under conditions of uncertainty, higher performance in the pursuit of goals, a high level of abstraction, and the merging of data from a multitude of sensors, with the ability for learning and adaptation in a heterogeneous environment for complex problems.

This Special Issue highlights current new research and applications, addresses problems encountered in control and complex optimization problems, and can address a wide range of intelligent system techniques, including neural networks, fuzzy logic, evolutionary strategy and genetic algorithms.

Prof. Dr. Ignacio Rojas
Guest Editor

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Keywords

Intelligent control
Complex optimization problem
Neural Networks
Evolutionary strategy
Genetic algorithms
Multidisciplinary optimization problems
Multi-criteria optimization

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Published Papers (9 papers)

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Research

15 pages, 2721 KiB

Open AccessArticle

Deep Q-Network Approach for Train Timetable Rescheduling Based on Alternative Graph

by Kyung-Min Kim, Hag-Lae Rho, Bum-Hwan Park and Yun-Hong Min

Appl. Sci. 2023, 13(17), 9547; https://doi.org/10.3390/app13179547 - 23 Aug 2023

Cited by 2 | Viewed by 1658

Abstract

The disturbance of local areas with complex railway networks and high traffic density not only impedes the efficient use of rail networks in those areas, but also propagates delays to the entire railway network. This has motivated research on train rescheduling problems in high-density local areas to minimize train delays by modifying their planned arrival and departure times. In this paper, we present a train rescheduling method based on Q-learning in reinforcement learning. More specifically, we used deep neural networks to approximate the action-value function, and the underlying Markov decision process (MDP) is based on the alternative graph formulation for the train rescheduling problem. In the proposed MDP formulation, the status of the alternative graph corresponding to the current schedule is defined as the state, and the alternative arc corresponds to the action the agent can take. The MDP is approximately solved via deep Q-learning in which deep neural networks are used to approximate the action-value function in Q-learning. Although the size of the alternative graph depends on the number of trains, our MDP formulation is independent of the number of trains, which makes the proposed method more scalable. The evaluation of the method was performed on a simple railway network and a real-world example in Seoul, South Korea, with randomly generated initial train schedules and train delays. The experimental result showed that the proposed method is comparable to the mixed-integer-linear-programming (MILP)-based exact approach with respect to the quality of the solution. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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20 pages, 1111 KiB

Open AccessArticle

Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments

by Miao Zhang, Peng Jiao, Yong Peng and Quanjun Yin

Appl. Sci. 2022, 12(3), 1646; https://doi.org/10.3390/app12031646 - 4 Feb 2022

Viewed by 1562

Abstract

Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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31 pages, 891 KiB

Open AccessArticle

Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents

by Andre Loechte, Ignacio Rojas Ruiz and Peter Gloesekoetter

Appl. Sci. 2022, 12(1), 274; https://doi.org/10.3390/app12010274 - 28 Dec 2021

Cited by 6 | Viewed by 1760

Abstract

The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal–air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc–air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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11 pages, 272 KiB

Open AccessArticle

A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem

by Maria Konieczka, Alicja Poturała, Jarosław Arabas and Stanisław Kozdrowski

Appl. Sci. 2021, 11(19), 9136; https://doi.org/10.3390/app11199136 - 30 Sep 2021

Cited by 4 | Viewed by 1938

Abstract

The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algorithms was performed in the discrete domain of the solution space, where we used the well-known knapsack problem in a variety of data correlations. The results obtained show that the proposed CMA-PBIL algorithm can perform better than standard PBIL in some cases. Therefore, the proposed algorithm can be a reasonable alternative to the PBIL algorithm in the discrete space domain. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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17 pages, 2497 KiB

Open AccessArticle

Burnout, Resilience, and COVID-19 among Teachers: Predictive Capacity of an Artificial Neural Network

by Juan Pedro Martínez-Ramón, Francisco Manuel Morales-Rodríguez and Sergio Pérez-López

Appl. Sci. 2021, 11(17), 8206; https://doi.org/10.3390/app11178206 - 3 Sep 2021

Cited by 22 | Viewed by 4327

Abstract

Emotional exhaustion, cynicism, and work inefficiency are three dimensions that define burnout syndrome among teachers. On another note, resilience can be understood as the ability to adapt to the environment and overcome adverse situations. In addition, COVID-19 has provided a threatening environment that has led to the implementation of resilience strategies to struggle with burnout and cope with the virus. The aim of this study was to analyze the relationship between resilience, burnout dimensions, and variables associated with COVID-19 through the design of an artificial neural network architecture. For this purpose, the Maslach Burnout Inventory-General Survey (MBI-GS), the Brief Resilience Coping Scale (BRCS), and a questionnaire on stress towards COVID-19 were administered to 419 teachers from secondary schools in southeastern Spain (292 females; 69.7%). The results showed that 30.8% suffered from burnout (high emotional exhaustion, high cynicism, and low professional efficacy) and that 38.7% had a high level of resilience, with an inverse relationship between both constructs. Likewise, we modelled an ANN able to predict burnout syndrome among 97.4% of teachers based on its dimensions, resilience, sociodemographic variables, and the stress generated by COVID-19. Our conclusions shed some light on the efficacy of relying on artificial intelligence in the educational field to predict the psychological situation of teachers and take early action. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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23 pages, 762 KiB

Open AccessFeature PaperArticle

A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond

by Surja Sanyal, Vikash Kumar Singh, Fatos Xhafa, Banhi Sanyal and Sajal Mukhopadhyay

Appl. Sci. 2021, 11(11), 5058; https://doi.org/10.3390/app11115058 - 30 May 2021

Cited by 7 | Viewed by 4101

Abstract

Food waste is currently a major challenge for the world. It is the precursor to several socioeconomic problems that are plaguing modern society. To counter and to, simultaneously, stand by the undernourished, surplus food redistribution has surfaced as a viable solution. Information and Communications Technology (ICT)-mediated food redistribution is a highly scalable approach and it percolates into the lives of the masses far better. Even if ICT is not brought into the picture, the presence of food surplus redistribution in developing countries such as India is scarce and is limited to only a few of the major cities. The discussion of a surplus food redistribution framework under strategic settings is a less discussed topic around the globe. This paper aims to address a surplus food redistribution framework under strategic settings, thereby facilitating a smoother exchange of surplus food in the smart cities of developing countries and beyond. As ICT is seamlessly available in smart cities, the paper aims to focus the framework in these cities. However, this can be extended beyond the smart cities to places with greater human involvement. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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21 pages, 1253 KiB

Open AccessArticle

Optimal COVID-19 Adapted Table Disposition in Hostelry for Guaranteeing the Social Distance through Memetic Algorithms

by Rubén Ferrero-Guillén, Javier Díez-González, Alberto Martínez-Guitiérrez and Rubén Álvarez

Appl. Sci. 2021, 11(11), 4957; https://doi.org/10.3390/app11114957 - 27 May 2021

Cited by 3 | Viewed by 2545

Abstract

The COVID-19 pandemic has challenged all physical interactions. Social distancing, face masks and other rules have reshaped our way of living during the last year. The impact of these measures for indoor establishments, such as education or hostelry businesses, resulted in a considerable organisation problem. Achieving a table distribution inside these indoor spaces that fulfilled the distancing requirements while trying to allocate the maximum number of tables for enduring the pandemic has proved to be a considerable task for multiple establishments. This problem, defined as the Table Location Problem (TLP), is categorised as NP-Hard, thus a metaheuristic resolution is recommended. In our previous works, a Genetic Algorithm (GA) optimisation was proposed for optimising the table distribution in real classrooms. However, the proposed algorithm performed poorly for high obstacle density scenarios, especially when allocating a considerable number of tables due to the existing dependency between adjacent tables in the distance distribution. Therefore, in this paper, we introduce for the first time, to the authors’ best knowledge, a Memetic Algorithm (MA) optimisation that improves the previously designed GA through the introduction of a Gradient Based Local Search. Multiple configurations have been analysed for a real hostelry-related scenario and a comparison between methodologies has been performed. Results show that the proposed MA optimisation obtained adequate solutions that the GA was unable to reach, demonstrating a superior convergence performance and an overall greater flexibility. The MA performance denoted its value not only from a COVID-19 distancing perspective but also as a flexible managing algorithm for daily table arrangement, thus fulfilling the main objectives of this paper. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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18 pages, 305 KiB

Open AccessArticle

Reduction of Annual Operational Costs in Power Systems through the Optimal Siting and Sizing of STATCOMs

by Oscar Danilo Montoya, Jose Eduardo Fuentes, Francisco David Moya, José Ángel Barrios and Harold R. Chamorro

Appl. Sci. 2021, 11(10), 4634; https://doi.org/10.3390/app11104634 - 19 May 2021

Cited by 13 | Viewed by 2345

Abstract

The problem of the optimal siting and placement of static compensates (STATCOMs) in power systems is addressed in this paper from an exact mathematical optimization point of view. A mixed-integer nonlinear programming model to present the problem was developed with the aim of minimizing the annual operating costs of the power system, which is the sum of the costs of the energy losses and of the installation of the STATCOMs. The optimization model has constraints regarding the active and reactive power balance equations and those associated with the devices’ capabilities, among others. To characterize the electrical behavior of the power system, different load profiles such as residential, industrial, and commercial are considered for a period of 24 h of operation. The solution of the proposed model is reached with the general algebraic modeling system optimization package. The numerical results indicate the positive effect of the dynamic reactive power injections in the power systems on annual operating cost reduction. A Pareto front was built to present the multi-objective behavior of the studied problem when compared to investment and operative costs. The complete numerical validations are made in the IEEE 24-, IEEE 33-, and IEEE 69-bus systems, respectively. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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18 pages, 323 KiB

Open AccessArticle

Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm

by Alejandra Paz-Rodríguez, Juan Felipe Castro-Ordoñez, Oscar Danilo Montoya and Diego Armando Giral-Ramírez

Appl. Sci. 2021, 11(10), 4418; https://doi.org/10.3390/app11104418 - 13 May 2021

Cited by 22 | Viewed by 2380

Abstract

This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm. Full article

(This article belongs to the Special Issue Advances in Intelligent System for Control and Complex Optimization Problems)

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