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Systems Modeling, Analysis and Optimization
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Systems Modeling, Analysis and Optimization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 21294

Special Issue Editors

Prof. Dr. Chuangyin Dang

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Guest Editor
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, China
Interests: game theory and applications; mathematical optimization and applications; systems modeling; analysis and optimization; mathematical modeling and analysis in economics and finance
Special Issues, Collections and Topics in MDPI journals
Dr. Siyang Gao

E-Mail Website
Guest Editor
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong
Interests: simulation modeling and optimization; applied probability; discrete event dynamic systems; healthcare management

Special Issue Information

Dear Colleagues,

Large-scale complex systems widely arise in real applications such as queueing systems, electric power grids, air and road traffic control systems, communication networks, manufacturing plants, supply chains, social networks, etc. With the increasing demand from stakeholders and the rapid development of information technologies and big data in recent years, there has been significant advancement in both the scale and complexity of the systems practitioners have to deal with. This creates challenges for describing and studying these systems, and in the meantime brings opportunities for the development of new modeling, analysis, control, and optimization techniques for them. This is a multidisciplinary theme that brings together simulation and computer modeling, data analytics, control theory, intelligent optimization, network technology, game theory, etc.

The aim of this Special Issue is to publish new theories, methods, algorithms, and applications in systems modeling, analysis, and optimization. The potential topics of interest are broad, and include but are not limited to simulation modeling, discrete event dynamic systems, Markov decision processes, reinforcement learning, optimization methods, social networks, metaheuristic algorithms, computation of equilibria in game theory, etc.

Prof. Dr. Chuangyin Dang
Dr. Siyang Gao
Guest Editors

Manuscript Submission Information

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Keywords

  • applied probability
  • simulation modeling and optimization
  • metaheuristics
  • robust optimization
  • large-scale optimization
  • game theory and applications
  • computation of Nash equilibrium and its refinements
  • computation of economic equilibrium

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Published Papers (7 papers)

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Research

22 pages, 4975 KiB  
Article
Fatigue Characteristics of Long-Span Bridge-Double Block Ballastless Track System
by Bin Yan, Jianghao Tian, Jie Huang and Ping Lou
Mathematics 2023, 11(8), 1792; https://doi.org/10.3390/math11081792 - 9 Apr 2023
Cited by 6 | Viewed by 2035
Abstract
The key issues in designing ballastless track for high-speed railway bridges are to reduce maintenance and improve track smoothness by understanding fatigue damage characteristics. This paper is based on the principle of bridge-rail interaction and train-track-bridge coupling dynamics, the refined simulation model of [...] Read more.
The key issues in designing ballastless track for high-speed railway bridges are to reduce maintenance and improve track smoothness by understanding fatigue damage characteristics. This paper is based on the principle of bridge-rail interaction and train-track-bridge coupling dynamics, the refined simulation model of bridge-CRTS I Bi-block ballastless track system is established by using the finite element method. The longitudinal force distribution law of CWR (Continuously Welded Rail) and the dynamic response characteristics of coupling systems are studied, based on the Miner rule and S-N curve. The fatigue characteristics of ballastless track system laying on long-span bridge under the dynamic train load and the effect of ballastless track system design parameters changes on fatigue characteristics are discussed. The results show that the extreme values of longitudinal force of CWR all appear in the middle of the bridge span or near the bridge bearing, and attention should be paid to the strength checking of CRW laying on long-span bridge. Under the dynamic train load, the fatigue life curve of rail on the bridge is relatively smooth and the minimum life of rail which is laying on continuous bridge decreases from 27.1 years to 17 years that which is laying on cable-stayed bridge. The life curve of track plate laying on continuous bridge is relatively smooth, and the life curve of track plate laying on cable-stayed bridge is related to the stiffness of elastic cushion, which decreases in a stepped manner, and there will be no fatigue failure on the track plate during service. The life curve of the baseplate is related to the type of bridge, the minimum life value of the baseplate appears near the bridge bearing, and there will be no fatigue failure on the baseplate during service. Increasing the stiffness of elastic cushion can effectively improve the fatigue life of track plate, and increasing the vertical stiffness of fasteners can enhance the connection between rail and track plate and improve the fatigue life of rail. The increase in train speed will increase the dynamic stress amplitude of track structure and reduce the fatigue life of the rail. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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33 pages, 5466 KiB  
Article
New Tilt Fractional-Order Integral Derivative with Fractional Filter (TFOIDFF) Controller with Artificial Hummingbird Optimizer for LFC in Renewable Energy Power Grids
by Emad A. Mohamed, Mokhtar Aly and Masayuki Watanabe
Mathematics 2022, 10(16), 3006; https://doi.org/10.3390/math10163006 - 20 Aug 2022
Cited by 26 | Viewed by 2703
Abstract
Recent advancements in renewable generation resources and their vast implementation in power sectors have posed serious challenges regarding their operation, protection, and control. Maintaining operating frequency at its nominal value and reducing tie-line power deviations represent crucial factors for these advancements due to [...] Read more.
Recent advancements in renewable generation resources and their vast implementation in power sectors have posed serious challenges regarding their operation, protection, and control. Maintaining operating frequency at its nominal value and reducing tie-line power deviations represent crucial factors for these advancements due to continuous reduction of power system inertia. In this paper, a new modified load frequency controller (LFC) method is proposed based on fractional calculus combinations. The tilt fractional-order integral-derivative with fractional-filter (TFOIDFF) is proposed in this paper for LFC applications. The proposed TFOIDFF controller combines the benefits of tilt, FOPID, and fractional filter regulators. Furthermore, a new application is introduced based on the recently presented artificial hummingbird optimizer algorithm (AHA) for simultaneous optimization of the proposed TFOIDFF parameters in the studied two-area power grids. The contribution of electric vehicle (EVs) is considered in the centralized control strategy using the proposed TFOIDFF controller. The performance of the proposed TFOIDFF controller has been compared with the existing tilt with filter, PID with filter, FOPID with filter and hybrid fractional-order with filter LFCs from the literature. Moreover, the AHA optimizer results are compared with the featured LFC optimization algorithms in the literature. The proposed TFOIDFF and AHA optimizer are validated against renewable energy fluctuations, load stepping, generation/loading uncertainty, and power-grid parameter uncertainty. The AHA optimizer is compared with the widely-used optimizers in the literature, including the PSO, ABC, BOA, and AEO optimizers at the IAE, ISE, ITAE, and ITSE objectives. For instance, the proposed AHA method has a minimized IAE after 34 iterations of 0.03178 compared to 0.03896 with PSO, 0.04548 with AEO, 0.04812 with BOA, and 0.05483 with ABC optimizer. Therefore, fast and better minimization of objective functions are achieved using the proposed AHA method. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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19 pages, 498 KiB  
Article
Robust Nonsmooth Interval-Valued Optimization Problems Involving Uncertainty Constraints
by Rekha R. Jaichander, Izhar Ahmad, Krishna Kummari and Suliman Al-Homidan
Mathematics 2022, 10(11), 1787; https://doi.org/10.3390/math10111787 - 24 May 2022
Cited by 3 | Viewed by 1880
Abstract
In this paper, Karush-Kuhn-Tucker type robust necessary optimality conditions for a robust nonsmooth interval-valued optimization problem (UCIVOP) are formulated using the concept of LU-optimal solution and the generalized robust Slater constraint qualification (GRSCQ). These Karush-Kuhn-Tucker type robust necessary conditions are shown to be [...] Read more.
In this paper, Karush-Kuhn-Tucker type robust necessary optimality conditions for a robust nonsmooth interval-valued optimization problem (UCIVOP) are formulated using the concept of LU-optimal solution and the generalized robust Slater constraint qualification (GRSCQ). These Karush-Kuhn-Tucker type robust necessary conditions are shown to be sufficient optimality conditions under generalized convexity. The Wolfe and Mond-Weir type robust dual problems are formulated over cones using generalized convexity assumptions, and usual duality results are established. The presented results are illustrated by non-trivial examples. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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34 pages, 9411 KiB  
Article
An Efficient Electric Charged Particles Optimization Algorithm for Numerical Optimization and Optimal Estimation of Photovoltaic Models
by Salah Kamel, Essam H. Houssein, Mohamed H. Hassan, Mokhtar Shouran and Fatma A. Hashim
Mathematics 2022, 10(6), 913; https://doi.org/10.3390/math10060913 - 13 Mar 2022
Cited by 11 | Viewed by 2193
Abstract
The electric charged particles optimization (ECPO) technique is inspired by the interaction (exerted forces) between electrically charged particles. A developed version of ECPO called MECPO is suggested in this article to enhance the capability of searching and balancing the exploitation and exploration phases [...] Read more.
The electric charged particles optimization (ECPO) technique is inspired by the interaction (exerted forces) between electrically charged particles. A developed version of ECPO called MECPO is suggested in this article to enhance the capability of searching and balancing the exploitation and exploration phases of the conventional ECPO. To let the search agent jumps out from the local optimum and avoid stagnation in the local optimum in the proposed MECPO, three different strategies in the interaction between ECPs are modified in conjunction with the conventional ECPO. Therefore, the convergence rate is enhanced and reaches rapidly to the optimal solution. To evaluate the effectiveness of the MECPO, it is executed on the test functions of the CEC’17. Furthermore, the MECPO technique is suggested to estimate the parameters of different photovoltaic models, such as the single-diode model (SDM), the double-diode model (DDM), and the triple-diode model (TDM). The simulation results illustrate the validation and effectiveness of MECPO in extracting parameters from photovoltaic models. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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38 pages, 13989 KiB  
Article
An Eagle Strategy Arithmetic Optimization Algorithm for Frequency Stability Enhancement Considering High Renewable Power Penetration and Time-Varying Load
by Ahmed. H. A. Elkasem, Salah Kamel, Mohamed H. Hassan, Mohamed Khamies and Emad M. Ahmed
Mathematics 2022, 10(6), 854; https://doi.org/10.3390/math10060854 - 8 Mar 2022
Cited by 26 | Viewed by 2873
Abstract
This study proposes a new optimization technique, known as the eagle strategy arithmetic optimization algorithm (ESAOA), to address the limitations of the original algorithm called arithmetic optimization algorithm (AOA). ESAOA is suggested to enhance the implementation of the original AOA. It includes an [...] Read more.
This study proposes a new optimization technique, known as the eagle strategy arithmetic optimization algorithm (ESAOA), to address the limitations of the original algorithm called arithmetic optimization algorithm (AOA). ESAOA is suggested to enhance the implementation of the original AOA. It includes an eagle strategy to avoid premature convergence and increase the populations’ efficacy to reach the optimum solution. The improved algorithm is utilized to fine-tune the parameters of the fractional-order proportional-integral-derivative (FOPID) and the PID controllers for supporting the frequency stability of a hybrid two-area multi-sources power system. Here, each area composites a combination of conventional power plants (i.e., thermal-hydro-gas) and renewable energy sources (i.e., wind farm and solar farm). Furthermore, the superiority of the proposed algorithm has been validated based on 23 benchmark functions. Then, the superiority of the proposed FOPID-based ESAOA algorithm is verified through a comparison of its performance with other controller performances (i.e., PID-based AOA, PID-based ESAOA, and PID-based teaching learning-based optimization TLBO) under different operating conditions. Furthermore, the system nonlinearities, system uncertainties, high renewable power penetration, and control time delay has been considered to ensure the effectiveness of the proposed FOPID based on the ES-AOA algorithm. All simulation results elucidate that the domination in favor of the proposed FOPID-based ES-AOA algorithm in enhancing the frequency stability effectually will guarantee a reliable performance. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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21 pages, 6844 KiB  
Article
Improvement of Trajectory Tracking by Robot Manipulator Based on a New Co-Operative Optimization Algorithm
by Mahmoud Elsisi, Hatim G. Zaini, Karar Mahmoud, Shimaa Bergies and Sherif S. M. Ghoneim
Mathematics 2021, 9(24), 3231; https://doi.org/10.3390/math9243231 - 14 Dec 2021
Cited by 40 | Viewed by 3228
Abstract
The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the [...] Read more.
The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies’ co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot’s response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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19 pages, 4830 KiB  
Article
Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms
by Mahmoud Elsisi, Minh-Quang Tran, Hany M. Hasanien, Rania A. Turky, Fahad Albalawi and Sherif S. M. Ghoneim
Mathematics 2021, 9(22), 2885; https://doi.org/10.3390/math9222885 - 12 Nov 2021
Cited by 69 | Viewed by 4788
Abstract
This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of [...] Read more.
This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques. Full article
(This article belongs to the Special Issue Systems Modeling, Analysis and Optimization)
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