Special Issue "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 7941

Special Issue Editor

Prof. Dr. Alma Y. Alanis
E-Mail Website
Guest Editor
Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), University of Guadalajara, 44160 Guadalajara, Mexico
Interests: intelligent control; bioinspired systems; learning control and intelligent systems

Special Issue Information

Dear Colleagues,

Nowadays, the relevance of artificial intelligence in our daily lives is evident, this has led to recent great advances in the development and implementation of bioinspired intelligent algorithms to solve a wide variety of real-world problems, in addition of the growing interest in the analysis of its mathematical properties. Although artificial intelligence has been developed mainly based on its applications, it is currently not possible to conceive it without its respective theoretical and algorithmic analysis, furthermore to its multidisciplinary motivation and applications. These applications vary from mechatronic systems, artificial vision, biomedical systems, energy systems, transportation, economics, classification, complex networks, economic systems, industry, transportation, among others. The aim of this special issue is to highlight recent advances in the development and application of bioinspired intelligent algorithms to solve real-world problems related to optimization, modeling and control, to provide a space for collaboration between researchers from different disciplines to solve real-world applications with their respective constraints and features in different fields of research. Papers with mathematical analysis and real-world application are particularly welcome.

Topics of interest include, but are not limited to:

  • Bioinspired intelligent algorithms for modeling
  • Bioinspired intelligent algorithms for control
  • Bioinspired intelligent algorithms for optimization
  • Data-driven bioinspired intelligent algorithms
  • Deep learning bioinspired intelligent algorithms
  • Mathematical analysis of bioinspired intelligent algorithms
  • Bioinspired intelligent algorithms applications to: robotics, dynamic systems, complex networks, classification, forecasting, biomedical systems, energy systems, industry, transportation, mechatronics, others.

Prof. Dr. Alma Y. Alanis
Guest Editor

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Published Papers (13 papers)

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Research

Article
A Metaheuristic Optimization Approach for Trajectory Tracking of Robot Manipulators
Mathematics 2022, 10(7), 1051; https://doi.org/10.3390/math10071051 - 25 Mar 2022
Viewed by 368
Abstract
Due to the complexity of manipulator robots, the trajectory tracking task is very challenging. Most of the current algorithms depend on the robot structure or its number of degrees of freedom (DOF). Furthermore, the most popular methods use a Jacobian matrix that suffers [...] Read more.
Due to the complexity of manipulator robots, the trajectory tracking task is very challenging. Most of the current algorithms depend on the robot structure or its number of degrees of freedom (DOF). Furthermore, the most popular methods use a Jacobian matrix that suffers from singularities. In this work, the authors propose a general method to solve the trajectory tracking of robot manipulators using metaheuristic optimization methods. The proposed method can be used to find the best joint configuration to minimize the end-effector position and orientation in 3D, for robots with any number of DOF. Full article
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Article
Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm
Mathematics 2022, 10(7), 1030; https://doi.org/10.3390/math10071030 - 23 Mar 2022
Viewed by 333
Abstract
Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered as a surrogate indicator of nutritional status. The whale optimization algorithm-based feature selection (WOFS) model could address the complex association between the clinical factors, and could further [...] Read more.
Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered as a surrogate indicator of nutritional status. The whale optimization algorithm-based feature selection (WOFS) model could address the complex association between the clinical factors, and could further combine with regression models for application. The present study aimed to demonstrate an optimal multifactor TSA-associated model, in order to interpret the complex association between TSA and clinical factors among HD patients. A total of 829 HD patients who met the inclusion criteria were selected for analysis. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted, stepwise, and WOFS models. Five features, albumin, age, creatinine, potassium, and HD adequacy index (Kt/V level), were selected from fifteen clinical factors by the WOFS model, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction. The WOFS model yielded the lowest Akaike information criterion (AIC) value, which indicated that the WOFS model could achieve superior performance in the multifactor analysis of TSA for HD patients. In conclusion, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients. Full article
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Article
Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation
Mathematics 2022, 10(7), 1014; https://doi.org/10.3390/math10071014 - 22 Mar 2022
Cited by 4 | Viewed by 511
Abstract
Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative [...] Read more.
Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation. Full article
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Article
Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control
Mathematics 2022, 10(5), 695; https://doi.org/10.3390/math10050695 - 23 Feb 2022
Viewed by 314
Abstract
For a comfortable thermal environment, the main parameters are indoor air humidity and temperature. These parameters are strongly coupled, causing the need to search for multivariable control alternatives that allow efficient results. Therefore, in order to control both the indoor air humidity and [...] Read more.
For a comfortable thermal environment, the main parameters are indoor air humidity and temperature. These parameters are strongly coupled, causing the need to search for multivariable control alternatives that allow efficient results. Therefore, in order to control both the indoor air humidity and temperature for direct expansion (DX) air conditioning (A/C) systems, different controllers have been designed. In this paper, a discrete-time neural inverse optimal control scheme for trajectories tracking and reduced energy consumption of a DX A/C system is presented. The dynamic model of the plant is approximated by a recurrent high-order neural network (RHONN) identifier. Using this model, a discrete-time neural inverse optimal controller is designed. Unscented Kalman filter (UKF) is used online for the neural network learning. Via simulation the scheme is tested. The proposed approach effectiveness is illustrated with the obtained results and the control proposal performance against disturbances is validated. Full article
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Article
Response Times Reconstructor Based on Mathematical Expectation Quotient for a High Priority Task over RT-Linux
Mathematics 2022, 10(1), 134; https://doi.org/10.3390/math10010134 - 02 Jan 2022
Viewed by 390
Abstract
Every computer task generates response times depending on the computer hardware and software. The response times of tasks executed in real-time operating systems such as RT-Linux can vary as their instances evolve even though they always execute the same algorithm. This variation decreases [...] Read more.
Every computer task generates response times depending on the computer hardware and software. The response times of tasks executed in real-time operating systems such as RT-Linux can vary as their instances evolve even though they always execute the same algorithm. This variation decreases as the priority of the tasks increases; however, the minimum and maximum response times are still present in the same task, and this complicates its monitoring, decreasing its level of predictability in case of contingency or overload, as well as making resource sizing difficult. Therefore, the need arises to propose a model capable of reconstructing the dynamics of response times for the instances of a task with high priority in order to analyze their offline behavior under specific working conditions. For this purpose, we develop the necessary theory to build the response time reconstruction model. Then, to test the proposed model, we set up a workbench consisting of a single board computer, PREEMPT_RT, and a high priority task generated by the execution of a matrix inversion algorithm. This work demonstrates the application of the theory in an experimental process, presenting a way to model and reconstruct the dynamics of response times by a high-priority task on RT-Linux. Full article
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Article
Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim® Validation
Mathematics 2021, 9(23), 3120; https://doi.org/10.3390/math9233120 - 03 Dec 2021
Cited by 1 | Viewed by 533
Abstract
This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control [...] Read more.
This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim® full vehicle model running through a double–lane change maneuver. Full article
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Article
Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization
Mathematics 2021, 9(22), 2971; https://doi.org/10.3390/math9222971 - 21 Nov 2021
Cited by 1 | Viewed by 510
Abstract
Thermoelectric generation systems (TEGSs) are used to convert temperature difference and heat flow into DC power based on the Seebeck theorem. The basic unit of TEGS is the thermoelectric module (TEM). TEGSs have gained increasing interest in the research fields of sustainable energy. [...] Read more.
Thermoelectric generation systems (TEGSs) are used to convert temperature difference and heat flow into DC power based on the Seebeck theorem. The basic unit of TEGS is the thermoelectric module (TEM). TEGSs have gained increasing interest in the research fields of sustainable energy. The output power from TEM is mostly reliant on differential temperature between the hot and cold sides of the TEM added to the value of the load. As such, a robust MPPT strategy (MPPTS) is required to ensure that the TEGS is operating near to the MPP while varying the operating conditions. Two main drawbacks may occur in the conventional MPPTSs: low dynamic response, such as in the incremental resistance (INR) method, and oscillations around MPP at steady state, such as in the hill climbing (HC) method. In the current research work, an optimized fractional MPPTS is developed to improve the tracking performance of the TEGS, and remove the two drawbacks of the conventional MPPTSs. The proposed strategy is based on fractional order control (FOC). The main advantage of FOC is that it offers extra flexible time and frequency responses of the control system consent for better and robust performance. The optimal parameters of the optimized fractional MPPTS are identified by a manta ray foraging optimization (MRFO). To verify the robustness of the MRFO, the obtained results are compared with ten other algorithms: particle swarm optimization; whale optimization algorithm; Harris hawks optimization; heap-based optimizer; gradient-based optimizer; grey wolf optimizer; slime mould algorithm; genetic algorithm; seagull optimization algorithm (SOA); and tunicate swarm algorithm. The maximum average cost function of 4.92934 kWh has been achieved by MRFO, followed by SOA (4.5721 kWh). The lowest STD of 0.04867 was also accomplished by MRFO. The maximum efficiency of 99.46% has been obtained by MRFO, whereas the lowest efficiency of 74.01% was obtained by GA. Finally, the main findings proved the superiority of optimized fractional MPPTS compared with conventional methods for both steady-state and dynamic responses. Full article
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Article
Performance of Gradient-Based Optimizer on Charging Station Placement Problem
Mathematics 2021, 9(21), 2821; https://doi.org/10.3390/math9212821 - 06 Nov 2021
Cited by 2 | Viewed by 540
Abstract
The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and [...] Read more.
The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer. Full article
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Article
Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization
Mathematics 2021, 9(19), 2499; https://doi.org/10.3390/math9192499 - 06 Oct 2021
Viewed by 707
Abstract
Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried [...] Read more.
Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried out using a deep learning approach. A new 64-layer architecture named 4-BSMAB derived from deep AlexNet is proposed. The proposed model was trained on CIFAR-100 dataset utilizing SoftMax classifier. Then, features were obtained from applied datasets with this pre-trained model. The obtained feature set was optimized with ant colony system (ACS) optimization technique. Various classifiers of SVM and KNN were used to perform gender classification utilizing the optimized feature set. Comprehensive experimentation was performed on gender classification datasets, and proposed model produced better results than the existing methods. The suggested model attained highest accuracy, i.e., 85.4%, and 92% AUC on MIT dataset, and best classification results, i.e., 93% accuracy and 96% AUC, on PKU-Reid dataset. The outcomes of extensive experiments carried out on existing standard pedestrian datasets demonstrate that the proposed framework outperformed existing pedestrian gender classification methods, and acceptable results prove the proposed model as a robust model. Full article
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Article
Learning Impulsive Pinning Control of Complex Networks
Mathematics 2021, 9(19), 2436; https://doi.org/10.3390/math9192436 - 01 Oct 2021
Viewed by 523
Abstract
In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used [...] Read more.
In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used in the analysis has unknown node self-dynamics, linear connections between nodes, where the impulsive dynamics add feedback control input only to the pinned nodes. The proposed controller consists of the linearization for the node dynamics and a reorder of the resulting quadratic Lyapunov function using the Rayleigh quotient. The learning part of the control is done with a discrete-time recurrent high order neural network used for identification of the pinned nodes, which is trained using an extended Kalman filter algorithm. A numerical simulation is included in order to illustrate the behavior of the system under the developed controller. For this simulation, a 20-node complex network with 5 different node dynamics is used. The node dynamics consists of discretized versions of well-known continuous chaotic attractors. Full article
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Article
Automatic Superpixel-Based Clustering for Color Image Segmentation Using q-Generalized Pareto Distribution under Linear Normalization and Hunger Games Search
Mathematics 2021, 9(19), 2383; https://doi.org/10.3390/math9192383 - 25 Sep 2021
Cited by 2 | Viewed by 527
Abstract
Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called [...] Read more.
Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics. Full article
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Article
Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer
Mathematics 2021, 9(18), 2313; https://doi.org/10.3390/math9182313 - 18 Sep 2021
Cited by 7 | Viewed by 916
Abstract
Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to [...] Read more.
Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively. Full article
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Article
A New Algorithm for Computing Disjoint Orthogonal Components in the Parallel Factor Analysis Model with Simulations and Applications to Real-World Data
Mathematics 2021, 9(17), 2058; https://doi.org/10.3390/math9172058 - 26 Aug 2021
Cited by 2 | Viewed by 570
Abstract
In this paper, we extend the use of disjoint orthogonal components to three-way table analysis with the parallel factor analysis model. Traditional methods, such as scaling, orthogonality constraints, non-negativity constraints, and sparse techniques, do not guarantee that interpretable loading matrices are obtained in [...] Read more.
In this paper, we extend the use of disjoint orthogonal components to three-way table analysis with the parallel factor analysis model. Traditional methods, such as scaling, orthogonality constraints, non-negativity constraints, and sparse techniques, do not guarantee that interpretable loading matrices are obtained in this model. We propose a novel heuristic algorithm that allows simple structure loading matrices to be obtained by calculating disjoint orthogonal components. This algorithm is also an alternative approach for solving the well-known degeneracy problem. We carry out computational experiments by utilizing simulated and real-world data to illustrate the benefits of the proposed algorithm. Full article
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