Research Article
Optimization & Operations Research
Tahereh Azizpour; Majid Yarahmadi
Abstract
In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles. Traditional quantum evolutionary algorithms have primarily been ...
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In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles. Traditional quantum evolutionary algorithms have primarily been implemented for discrete and binary decision variables. The proposed method has been designed as a novel continuous quantum evolutionary optimization algorithm tailored to problems with continuous decision variables. To assess the algorithm’s performance, several numerical experiments are conducted, and the simulated results are compared with the Grey Wolf Optimizer and Magnet Fish Optimization search algorithm. The simulation results indicate that the proposed algorithm can approximate the optimal solution more accurately than the two compared algorithms.
Research Article
Control Theory & Systems
Ali Dehghani Filabadi; Hossein Nahid Titkanlue
Abstract
This paper addresses multi-attribute group decision-making (MAGDM) where linguistic assessments are represented by both positive and negative interval type-2 fuzzy numbers (IT2FNs), capturing the intrinsic uncertainty of group evaluations more accurately. We introduce a novel ranking method ...
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This paper addresses multi-attribute group decision-making (MAGDM) where linguistic assessments are represented by both positive and negative interval type-2 fuzzy numbers (IT2FNs), capturing the intrinsic uncertainty of group evaluations more accurately. We introduce a novel ranking method for IT2FNs that simultaneously utilizes the mean and standard deviation of the upper and lower membership functions, as well as the IT2FN's height. This enhances its discriminatory capability. The theoretical foundations of this ranking— encompassing zero, unity, and symmetry properties— are rigorously established, and its superiority over existing techniques is demonstrated through comparative analyses on seven benchmark datasets. Building on this ranking, we develop an integrated fuzzy MAGDM framework that can handle both positive and negative IT2FN assessments for criteria and weights. The framework’s practicality and effectiveness are validated through two case studies: one with exclusively positive linguistic terms and another with mixed positive and negative scales. Results indicate that the proposed ranking and decision framework yield more rational and robust group decisions under substantial uncertainty. They outperform conventional fuzzy methods and offer a nuanced solution for real-world MAGDM scenarios.
Research Article
Optimization & Operations Research
Farzad Rahpeymaii; Majid Rostami
Abstract
The conjugate gradient ({CG}) method is one of the simplest and most widely used approaches for unconstrained optimization, and our focus is on two-dimensional problems with numerous practical applications. We devise three hybrid {CG} methods in which the hybrid parameter is constructed from the Barzilai–Borwein ...
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The conjugate gradient ({CG}) method is one of the simplest and most widely used approaches for unconstrained optimization, and our focus is on two-dimensional problems with numerous practical applications. We devise three hybrid {CG} methods in which the hybrid parameter is constructed from the Barzilai–Borwein process, and in these hybrids, the weaknesses of each constituent method are mitigated by the strengths of the others. The conjugate gradient parameter is formed as a linear combination of two well-known CG parameters, blended by a scalar, enabling our new methods to solve the targeted problems efficiently. Under mild assumptions, we establish the descent property of the generated directions and prove the global convergence of the hybrid schemes. Numerical experiments on ten practical examples indicate that the proposed hybrid {CG} methods outperform standard {CG} methods for two-dimensional unconstrained optimization.
Research Article
Control Theory & Systems
Subramani Magudeeswaran; Muthurathinam Sivabalan; Mehmet Yavuz; Dharmendra Kumar Singh; Kannimuthu Giridharan
Abstract
In this study, we fabricate and investigate a three-species intraguild predation model with a ratio-dependent functional response. We also incorporate harvesting efforts into both intraguild prey and intraguild predators. Then, we analyze the dynamical behavior ...
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In this study, we fabricate and investigate a three-species intraguild predation model with a ratio-dependent functional response. We also incorporate harvesting efforts into both intraguild prey and intraguild predators. Then, we analyze the dynamical behavior of the proposed model by taking the harvesting rate as the bifurcation parameter. We precisely outline the prerequisites for the proposed model's existence, stability, and bifurcation near the equilibrium points. It contributes to a better understanding of the impacts of harvesting on the survival or extinction of one or more species in the proposed model. Furthermore, we derive the suggested model's bionomic equilibrium and optimum harvesting policy by using the \textit{Pontryagin's maximum principle}. Finally, we provide some numerical simulations to validate the analytical results. In addition, we give some graphical representations to validate our results.
Research Article
Control Theory & Systems
Sommayeh Sheykhi; Mashallah Matinfar; Mohammad Arab Firoozjaee
Abstract
The advection-dispersion, variable-order differential equations have a vast application in fluid physics and energy systems. In this study, we propose a Ritz-approximation method using shifted Legendre polynomials to construct approximate numerical solutions for these equations. The ...
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The advection-dispersion, variable-order differential equations have a vast application in fluid physics and energy systems. In this study, we propose a Ritz-approximation method using shifted Legendre polynomials to construct approximate numerical solutions for these equations. The proposed method discretizes the original problem, converting it into a system of nonlinear algebraic equations that can be solved numerically at selected points. We discuss the error characteristics of the proposed method. For validation, the presented examples are compared with exact solutions and with prior results. The results indicate that the proposed method is highly effective.
Research Article
Control Theory & Systems
Mohammad Rashki-Ghalehno; Seyed Mehdi Mirhosseini-Alizamini; Bashir Naderi
Abstract
This paper introduces a robust hybrid adaptive control framework for stabilizing chaotic systems under persistent, potentially large time delays. The controller is based on an enhanced Lyapunov–Krasovskii functional that integrates an energy-capturing integral term with a bounded trigonometric ...
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This paper introduces a robust hybrid adaptive control framework for stabilizing chaotic systems under persistent, potentially large time delays. The controller is based on an enhanced Lyapunov–Krasovskii functional that integrates an energy-capturing integral term with a bounded trigonometric term. The integral term accounts for historical effects by quantifying cumulative energy over the delay period, while the trigonometric term attenuates nonlinear oscillations. Embedding these components in a single control law yields stabilization of all state variables to the equilibrium despite substantial delays. We establish Uniform Ultimate Boundedness, showing that trajectories enter a compact neighborhood of the equilibrium after a finite transient and subsequently converge. Adjustable gains enable practitioners to determine the convergence radius and the size of the attraction region according to practical requirements. The method is validated on the delayed Lorenz system; simulations with a 20-second delay demonstrate rapid convergence to a small neighborhood of the equilibrium, with the Lyapunov functional derivative remaining non-positive. A comparative study with established controllers underscores the proposed approach’s favorable trade-offs among computational cost, oscillation suppression, and explicit stability guarantees. Overall, the proposed framework delivers a practical, robust, and high-performance solution for controlling chaotic systems in the presence of large time delays.
Research Article
Optimization & Operations Research
Maedeh Shahabi; Freydoon Rahbarnia
Abstract
In irregular coloring, each vertex is labeled with a unique color code, a tuple consisting of its assigned color and the number of neighbors in each color class. This work proposes a local search algorithm as a metaheuristic approach to the irregular face coloring problem in planar graphs, ...
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In irregular coloring, each vertex is labeled with a unique color code, a tuple consisting of its assigned color and the number of neighbors in each color class. This work proposes a local search algorithm as a metaheuristic approach to the irregular face coloring problem in planar graphs, with a particular focus on fullerene molecular structures. Additionally, a linear programming model is utilized to validate the performance of the proposed algorithm. The methodology demonstrates efficient solutions for irregular coloring in fullerene graphs, bridging combinatorial optimization with practical applications in chemistry and materials science.
Research Article
Optimization & Operations Research
Zahra Mohammadhashemi; Khatere Ghorbani-Moghadam; Safora Allahy; Sepehr Ghazinoory
Abstract
This study employs a two-stage analytical framework to assess efficiency, comprising a standard SBM evaluation and a novel weighted SBM model. Unlike conventional SBM-DEA applications, the proposed weighted model uses an enhanced slack-based mechanism that prioritizes strategic inputs (R&D investment, ...
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This study employs a two-stage analytical framework to assess efficiency, comprising a standard SBM evaluation and a novel weighted SBM model. Unlike conventional SBM-DEA applications, the proposed weighted model uses an enhanced slack-based mechanism that prioritizes strategic inputs (R&D investment, number of employees, and funding) and clearly distinguishes input redundancies (e.g., excessive R&D expenditure or staffing) from output deficiencies (e.g., weak revenue performance). This separation yields more precise and targeted diagnostic insights. Additionally, the model incorporates sector-specific efficiency differentiation, supported by ANOVA, enabling assessment of cross-firm inefficiencies and their statistical significance in terms of systemic versus sector-specific phenomena. The methodology is applied to a distinctive panel of 146 technology-based firms (TBFs) in Iranian science and technology parks from 2021–2023, a context rarely explored with DEA in emerging markets. The study combines quantitative DEA results from both models with qualitative follow-up analyses of factors such as marketing strategies, private investment initiatives, and certification achievements, producing a robust mixed-methods approach and actionable policy recommendations. A comparative analysis reveals that fully efficient firms comprise 2.7\% under the unweighted model and 3.4\% under the weighted model, indicating that weighting yields a small, non-significant change in overall efficiency. About 97.3\% of firms display efficiency gaps due to input redundancies or output shortfalls. Sectoral tests show no statistically significant inter-sector differences, pointing to systemic inefficiencies across industries. Qualitative insights identify firm-level success factors—effective marketing, certification, and investment strategies—that align with the detected inefficiency patterns. Collectively, these findings offer measurable strategies for improvement, such as reducing redundant investment and enhancing revenue-generation mechanisms, to inform evidence-based policy aimed at the commercialization and growth of TBFs in emerging markets.
Research Article
Machine Learning & Data Science in Optimization
Saeed Mirzajani; Majid Roohi
Abstract
The prediction of chaotic time series is essential for understanding highly nonlinear and sensitive systems, with the Lorenz system serving as a standard benchmark due to its intricate and non-periodic dynamics. Classical forecasting approaches often struggle to capture such irregularities, ...
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The prediction of chaotic time series is essential for understanding highly nonlinear and sensitive systems, with the Lorenz system serving as a standard benchmark due to its intricate and non-periodic dynamics. Classical forecasting approaches often struggle to capture such irregularities, motivating a shift toward deep learning–based strategies. In this study, we develop two hybrid models—Feedback Long Short-Term Memory (FB-LSTM) and Feedback Variational Stacked LSTM (FBVS-LSTM), specifically designed for multivariate prediction of the Lorenz system. By embedding feedback structures into LSTM networks, the proposed methods deliver enhanced short-term prediction performance without substantial computational costs. Comparative simulations indicate that our frameworks surpass traditional RNNs and baseline LSTM models, achieving prediction accuracies up to 94%. These findings indicate that feedback-enhanced architectures offer effective and practical tools for forecasting chaotic systems, with potential applications in both scientific research and engineering practice.
Research Article
Optimization & Operations Research
Mohamed Kouadria; Halim Zeghdoudi; Mohammed El-Arbi Khalfallah
Abstract
This study proposes the New Two-Parameter Weibull–Lindley Distribution (NTPWLD), a flexible lifetime model generated through a transformation of a one-parameter baseline survival function. Owing to its general structure, the NTPWLD accommodates diverse hazard rate shapes, including increasing, ...
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This study proposes the New Two-Parameter Weibull–Lindley Distribution (NTPWLD), a flexible lifetime model generated through a transformation of a one-parameter baseline survival function. Owing to its general structure, the NTPWLD accommodates diverse hazard rate shapes, including increasing, decreasing, and bathtub forms, and captures both light- and heavy-tailed behaviors relevant to survival analysis, engineering reliability, and biomedical applications. The work provides a full mathematical treatment of the distribution, deriving closed-form expressions for its density, distribution, survival, hazard, and quantile functions, along with ordinary and incomplete moments, the moment generating function, mean deviations, and Rényi entropy. Several reliability measures, such as mean residual life and stress–strength reliability, are also obtained. Parameter estimation is examined under various inferential approaches, with particular focus on maximum likelihood estimation. A Monte Carlo simulation study shows that the maximum likelihood estimator performs well across settings, displaying low bias, stability, and consistency. To incorporate uncertainty in lifetime data, fuzzy reliability measures are constructed using Zadeh’s extension principle and α-cut techniques. Applications to two real datasets demonstrate that the NTPWLD provides superior goodness-of-fit compared with several competing models based on AIC, BIC, AICC, and −2 log L, highlighting its practical value in both precise and fuzzy reliability environments.
Research Article
Mathematics & Theoretical Foundations
Hajir Afzali
Abstract
This study develops a nonlinear dynamic modeling framework to analyze and predict performance behavior in industrial environments using competitive-intelligence-related variables. Four organizational resource components are formulated as elements of a discrete-time state vector, and their influence on ...
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This study develops a nonlinear dynamic modeling framework to analyze and predict performance behavior in industrial environments using competitive-intelligence-related variables. Four organizational resource components are formulated as elements of a discrete-time state vector, and their influence on system output is modeled through a nonlinear state-transition function. Empirical observations collected from a steel manufacturing company were used to identify the unknown dynamics through a feed-forward artificial neural network trained via a gradient-based optimization procedure. Reliability of the measurement instrument was verified using Cronbach’s alpha coefficients of 0.92 and 0.86 for the independent and dependent constructs, respectively. The identified model demonstrates stable convergence, with the minimum prediction error achieved near iteration 1500, and outperforms a linear baseline in mean-squared error and correlation accuracy. The proposed formulation provides a mathematically oriented approach for reconstructing performance-driven system behavior and establishes a foundation for future extensions involving adaptive estimation, robust analysis, and optimal control strategies in industrial systems.
Research Article
Control Theory & Systems
Hadi Sharifi; Mostafa Akhavan-Safar; Mohammad Mohsen Sadr
Abstract
Enterprise architecture (EA) offers an integrated framework for strategic planning and organizational governance. Implementing EA effectively requires prioritizing a concise set of criteria within a complex system, leveraging mathematical modeling and optimization to inform decisions under uncertainty. ...
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Enterprise architecture (EA) offers an integrated framework for strategic planning and organizational governance. Implementing EA effectively requires prioritizing a concise set of criteria within a complex system, leveraging mathematical modeling and optimization to inform decisions under uncertainty. This study introduces a hierarchical decision-making approach using Analytic Hierarchy Process (AHP) to extract and weight the most impactful criteria from an extensive literature base and expert opinions, with a focus on control-theoretic and optimization perspectives. Using insights from 18 experts from various fields and the proposed approach, key criteria of successful enterprise architecture deployment were identified and quantified: commitment (0.1143), governance (0.1082), infrastructure (0.0751), organizational management (0.0589), and senior management support (0.0484). The methodology integrates weights with objective-function considerations, sensitivity analyses, and optimization-oriented interpretations to ensure robust prioritization under uncertainty. The resulting framework supports decision-makers in (i) controlling and steering EA initiatives, (ii) optimizing resource allocation and process efficiencies, and (iii) designing data-driven, scenario-based decision models for dynamic organizational environments. These findings offer actionable guidance for managers aiming to enhance performance, reduce costs, and secure competitive advantage through disciplined governance, rigorous modeling, and evidence-based decision support.
Research Article
Control Theory & Systems
Masrour Dowlatabadi; Maryam Nikbakht
Abstract
This study analyzes the growth dynamics of melanoma tumor cells and develops a model predictive controller (MPC) using four well-known optimizers to suppress tumor growth, proposing an MPC framework that integrates multiple metaheuristic algorithms for regulating tumor size. All modelling, control design, ...
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This study analyzes the growth dynamics of melanoma tumor cells and develops a model predictive controller (MPC) using four well-known optimizers to suppress tumor growth, proposing an MPC framework that integrates multiple metaheuristic algorithms for regulating tumor size. All modelling, control design, and simulations are performed in MATLAB, and results indicate that a PSO-based MPC offers satisfactory response and rapid convergence, achieving effective tracking and disturbance rejection. The study assumes precise drug dosing is feasible and demonstrates substantial tumor-size reduction through the integration of MPC with metaheuristic optimization. Simulation findings reveal that the PSO-based MPC achieves notable improvement in tumor reduction and overall control performance, outperforming other metaheuristic approaches, as evidenced by comparative error metrics: ITAE ≈ 1.9377 × 10^3, IAE ≈ 244.45, MSE ≈ 4.6863 × 10^3.
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