Results in Control and Optimization最新文献

英文中文

Divided opposition strategy in particle swarm framework for constrained optimization problem

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-12-06 DOI: 10.1016/j.rico.2024.100508

Sarika Jain , Rekha Rani , Pradeep Jangir , Seyed Jalaleddin Mousavirad , Ali Wagdy Mohamed

In nature inspired algorithms, population initialization techniques play an important role to find an optimal solution. In this study, we proposed a novel population initialization technique Divided opposition-based learning Particle Swarm Optimization (D-PSO). This technique is inspired by Opposition Based Learning (OBL). D-PSO is a technique in which elements of initial population are uniformly cover the search space so the possibility of obtaining the optimal solution is highest. To validate the results D-PSO is tested on 16 benchmark functions for dimensions 10 and 30 and 12 CEC22 functions along with standard PSO, OBL-PSO, I-PSO. In standard PSO elements of initial population is randomly generated and in OBL-PSO elements of initial population are generated using OBL technique. I-PSO generate initial population elements using improved OBL technique. D-PSO gives better outcomes for all benchmark functions for dimension 10, 30 and 10 CEC22 function out of 12 as compared to other initialization techniques. To measure the significance of results a statistical analysis is also done in this study. Complexity analysis and convergence analysis is also measured for both set of benchmark functions. The convergence behavior of D-PSO for all benchmark function for dimension 10, 30 and 10 CEC22 function is best as compared to other initialization technique.

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引用次数: 0

Optimal control governed impulsive neutral differential equations

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-12-01 DOI: 10.1016/j.rico.2024.100505

Oscar Camacho , René Erlin Castillo , Hugo Leiva

We derive Pontryagin’s Maximum Principle for optimal control problems characterized by nonlinear impulsive neutral type differential equations. Our method utilizes the Dubovitskii–Milyutin theory, assuming that the linear variational impulsive differential equation along the optimal solution is exactly controllable. This principle offers necessary conditions for identifying optimal solutions.

引用次数: 0

TSP solution using an exact model based on the branch flow formulation and automatic cases generation via the Julia software

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-12-01 DOI: 10.1016/j.rico.2024.100507

Oscar Danilo Montoya , Walter Gil-González , Luis Fernando Grisales-Noreña , Rubén Iván Bolaños , Jorge Ardila-Rey

The traveling salesman problem (TSP) is a classical optimization problem with practical applications in logistics, transportation, and network design. This research proposes an efficient mixed-integer linear programming (MILP) model based on the branch flow formulation which prevents the formation of sub-tours during the solution process and guarantees valid optimal routes. Implemented in Julia with the JuMP optimization package and the HiGHS solver, the model achieves high computational efficiency. Unlike classical models, the branch flow formulation ensures a quadratic constraint growth, rather than an exponential one, significantly enhancing scalability. Benchmark tests on various instances (Eil51, Eil76, KroA100) demonstrate results comparable to state-of-the-art combinatorial optimizers, and six new TSP instances, ranging from 50 to 300 cities, validate the model’s performance. This scalable and robust approach is well-suited for real-world applications in supply chain management, network optimization, and urban planning, and it shows potential for future extensions to dynamic or multi-objective TSP variants.

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引用次数: 0

Optimal fuzzy robust state feedback control for a five DOF active suspension system 五自由度主动悬架系统的最优模糊鲁棒状态反馈控制

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-12-01 DOI: 10.1016/j.rico.2024.100504

M.J. Mahmoodabadi , N. Nejadkourki , M. Yousef Ibrahim

Active suspension systems are integral to modern vehicles, enhancing driving comfort by addressing road irregularities and isolating the vehicle's interior from vibrations. In this research, we construct an active suspension system with five degrees of freedom (DOF) and find the best fuzzy robust state feedback controller to control it. While designing the state feedback controller, we considered the initial errors in the relative displacement and acceleration as well as their derivatives. A singleton fuzzifier, center average russification, and product inference engine are all control parameters managed by a fuzzy system. Optimization using the Sine Cosine Algorithm (SCA) is then used to determine the optimal gains for the controller that has been constructed. The technique uses two objective functions for depreciation: the body's acceleration, the relative displacement between the tire and sprung mass. Results show that the suggested active suspension system is better than that of previous studies.

主动悬架系统是现代车辆不可或缺的一部分，通过解决道路不规则性和隔离车辆内部振动来提高驾驶舒适性。在本研究中，我们构造了一个五自由度主动悬架系统，并找到了最佳的模糊鲁棒状态反馈控制器对其进行控制。在设计状态反馈控制器时，考虑了相对位移和加速度的初始误差及其导数。单个模糊化、中心平均化和产品推理机都是由模糊系统管理的控制参数。然后使用正弦余弦算法（SCA）进行优化，以确定已构建的控制器的最佳增益。该技术使用两个目标函数进行折旧：身体的加速度，轮胎和弹簧质量之间的相对位移。结果表明，所提出的主动悬架系统优于以往的研究。

引用次数: 0

A pursuit-evasion game robot controller design based on a neural network with an improved optimization algorithm 基于神经网络和改进优化算法的追逐-逃避游戏机器人控制器设计

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-11-23 DOI: 10.1016/j.rico.2024.100503

Mustafa Wassef Hasan, Luay G. Ibrahim

A pursuit-evasion game (PEG) is a type of game that utilizes one or several cooperative pursuers to capture one or several evaders. The PEG game concept has been used in different multi-robot applications such as transportation or navigation applications, search and rescue, surveillance applications such as collision avoidance and air traffic control systems, multi-defense applications such as missile guidance systems, and medical applications such as analyzing biological behaviors. Regardless of the benefits of PEG, one of the main drawbacks of such systems is the computational burden and the immense time required to learn such systems. For this reason, this work proposes a neural network game based on the pursuit-evasion game, where the leader (evader) robot tries to eat several particles/apples distributed inside a closed game environment with boundary and inner obstacles. In contrast, a follower (pursuer) robot tries to capture the leader robot and stop the particle-eating process. The leader and follower robots were designed based on a differential two-wheel robot (DTWR). The neural network is presented to control and learn the leader and follower robot directions with respect to the boundary and inside obstacles in the game environment. The neural network weights are learned using an improved sine cosine algorithm based on chaotic theory (ISCACT). The ISCACT is proposed to solve and avoid the proposed game of being trapped in the local minimum problem. The ISCACT is tested based on five multimodal benchmark functions. The ISCACT has been used in two cases, the first case arises when ISCACT is used in the follower robot’s learning process. In the second case, the ISCACT has been used in the leader robot’s learning process. The results for the first and second cases prove the superiority of the ISCACT compared with other existing works in enhancing the PEG performance time and reducing the computational burden for multi-robot applications.

追逐-逃避游戏（PEG）是一种利用一个或多个合作追逐者捕捉一个或多个逃避者的游戏。PEG 游戏概念已被用于不同的多机器人应用中，如运输或导航应用、搜索和救援、监控应用（如避免碰撞和空中交通管制系统）、多重防御应用（如导弹制导系统）以及医疗应用（如分析生物行为）。尽管 PEG 有诸多优点，但其主要缺点之一是学习此类系统所需的计算负担和大量时间。因此，本研究提出了一种基于 "追逐-逃避 "博弈的神经网络博弈。在这种博弈中，领跑者（逃避者）机器人试图吃掉分布在一个封闭博弈环境中的多个颗粒/苹果，该环境具有边界和内部障碍物。与此相反，跟随者（追逐者）机器人则试图捕获领导者机器人并阻止吃颗粒的过程。领导者和追随者机器人是基于差分双轮机器人（DTWR）设计的。神经网络用于控制和学习领跑者和追随者机器人在游戏环境中相对于边界和内部障碍物的方向。使用基于混沌理论的改进正弦余弦算法（ISCACT）学习神经网络权重。提出 ISCACT 的目的是为了解决和避免所提出的游戏陷入局部最小值问题。根据五个多模态基准函数对 ISCACT 进行了测试。ISCACT 在两种情况下使用，第一种情况是 ISCACT 用于跟随机器人的学习过程。在第二种情况下，ISCACT 被用于领导机器人的学习过程。第一种情况和第二种情况的结果证明，与其他现有研究相比，ISCACT 在提高 PEG 性能时间和减少多机器人应用的计算负担方面更具优势。

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引用次数: 0

Prediction of seam strength of cotton canvas fabric using fuzzy logic 利用模糊逻辑预测棉帆布的接缝强力

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-11-22 DOI: 10.1016/j.rico.2024.100502

Elias Khalil , Mahmuda Akter

This study investigates the application of fuzzy logic in predicting seam strength in cotton plain canvas fabric, focusing on both warp and weft directions. The precise prediction of seam strength is crucial for manufacturers to uphold quality standards, enhance production efficiency, and minimize waste. The fuzzy logic model in this study uses thread linear density and stitch per inch as input parameters and warp and weft seam strength as output variables. The modeling was conducted using MATLAB, specifically utilizing the Mamdani fuzzy inference system with triangle membership functions. The fuzzy logic model was found to be very accurate, as shown by coefficients of determination (R²) of 0.9841 for the warp way and 0.9888 for the weft way, along with correlation coefficients (R) of 0.992 and 0.9944. The mean absolute percentage error (MAPE) was calculated to be 4.8719 % for the warp way and 4.7561 % for the weft way, each below 5 %, underscoring the model's reliability and robustness in seam strength prediction. This research provides findings with substantial implications for the textile industry, where the application of predictive models is on the rise to enhance production efficiency and product quality. Manufacturers can improve their ability to forecast regarding fabric properties and adjust production processes through the implementation of fuzzy logic models. This approach is consistent with current industry trends emphasizing automation and digitalization, wherein predictive models are essential for facilitating smart manufacturing and quality control.

本研究探讨了模糊逻辑在棉平纹帆布接缝强力预测中的应用，重点是经向和纬向。接缝强力的精确预测对于制造商维护质量标准、提高生产效率和减少浪费至关重要。本研究中的模糊逻辑模型以线性密度和每英寸针数作为输入参数，以经纬缝合强度作为输出变量。建模使用了 MATLAB，特别是使用了带有三角形成员函数的马姆达尼模糊推理系统。经向和纬向的判定系数（R2）分别为 0.9841 和 0.9888，相关系数（R）分别为 0.992 和 0.9944，表明模糊逻辑模型非常精确。经向和纬向的平均绝对百分比误差（MAPE）分别为 4.8719 % 和 4.7561 %，均低于 5 %，这表明该模型在接缝强度预测方面具有可靠性和稳健性。这项研究为纺织业提供了具有重大意义的发现，因为在纺织业中，预测模型的应用正在不断增加，以提高生产效率和产品质量。制造商可以通过实施模糊逻辑模型，提高对织物特性的预测能力，并调整生产流程。这种方法符合当前强调自动化和数字化的行业趋势，其中预测模型对于促进智能制造和质量控制至关重要。

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引用次数: 0

Simultaneous multi-objective optimization method for trading indicators 交易指标的多目标同步优化方法

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-11-21 DOI: 10.1016/j.rico.2024.100501

Nhat M. Nguyen, Minh Tran

This paper proposes a novel framework for optimizing trading indicators using a multi-objective Particle Swarm Optimization approach. By simultaneously optimizing multiple technical indicators, the method overcomes the limitations of single-objective optimization and complex strategies, resulting in a more robust trading approach. Experiments on VN30-Index daily data demonstrate that the optimized strategy outperforms benchmark and buy-and-hold strategies in terms of returns and Sharpe ratios. Our findings prove that the multi-objective Particle Swarm Optimization method efficiently balances the complexity to combine various technical indicators in a way that keeps the logic of the strategy simple. The technique not only reduces the risks of relying on one indicator but also reduces behavioral influences in the stock selection process. Furthermore, our study adds to the literature a simple and effective method that helps traders identify profitable investment opportunities in different market scenarios.

本文提出了一种利用多目标粒子群优化法优化交易指标的新框架。通过同时优化多个技术指标，该方法克服了单目标优化和复杂策略的局限性，从而产生了一种更稳健的交易方法。对 VN30 指数每日数据的实验表明，优化后的策略在收益和夏普比率方面优于基准策略和买入并持有策略。我们的研究结果证明，多目标粒子群优化方法有效地平衡了各种技术指标的复杂性，使策略逻辑保持简单。该技术不仅降低了依赖单一指标的风险，还减少了选股过程中的行为影响。此外，我们的研究为文献增添了一种简单有效的方法，可帮助交易者在不同的市场情况下识别有利可图的投资机会。

引用次数: 0

A sustainable three-stage production inventory model with trapezoidal demand and time-dependent holding cost 具有梯形需求和随时间变化的持有成本的可持续三阶段生产库存模型

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-11-19 DOI: 10.1016/j.rico.2024.100493

Suvetha R. , Rangarajan K. , Rajadurai P.

The economic vitality of a nation is contingent upon the advancement of its manufacturing sectors, given their pivotal role in fostering economic growth. These industries frequently encounter challenges such as mitigating deterioration rates, enhancing revenue and reducing overall costs to optimize profits. Consequently, should an item deteriorate while in stock within manufacturing facilities, it results in a gradual escalation of holding costs and total expenses. In this paper discusses determining the most effective production policy for items prone to degradation, analyzing depreciating items using three-stage production inventory models with trapezoidal demand to minimize holding costs based on time-dependent factors in the manufacturing sector. This model aims to decrease overall costs and production time periods, contrasting with the higher cost values of the price-based constant method. Mathematical formulas were developed using MATLAB R2023b to validate the models findings and minimize the inventory systems cost.

一个国家的经济活力取决于其制造业的发展，因为制造业在促进经济增长方面发挥着举足轻重的作用。这些行业经常遇到各种挑战，如降低变质率、增加收入和降低总体成本以优化利润。因此，如果生产设施中的库存物品变质，就会导致持有成本和总支出逐步上升。本文讨论了如何针对易降解物品确定最有效的生产政策，利用梯形需求的三阶段生产库存模型分析折旧物品，从而根据制造业中的时间相关因素最大限度地降低持有成本。该模型旨在降低总体成本和缩短生产周期，与基于价格的恒定方法的较高成本值形成鲜明对比。使用 MATLAB R2023b 开发了数学公式，以验证模型的结论，并最大限度地降低库存系统成本。

引用次数: 0

Optimization of a high gain observer for feedback linearization control 优化反馈线性化控制的高增益观测器

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-11-19 DOI: 10.1016/j.rico.2024.100494

Nadia Bounouara, Mouna Ghanai, Kheireddine Chafaa

In this paper, a continuous–discrete time observer using an optimized high gain is proposed for a robotic manipulator where the output is time sampled. The main contribution of this approach is to improve the value of the high gain that corresponds to the minimum value of the cost function by using some metaheuristic algorithms. The observer is characterized by an optimal high gain that is optimized by biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) method and genetic algorithms (GA). Through this investigation, it is proven that the best optimization results are obtained through the process based on the BBO algorithm. BBO is a relatively new nature-inspired optimization algorithm used to find the best and optimal value for an optimization problem. The introduced method is implemented in two steps. In the first step the high gain is optimized in an off-line way by the BBO algorithm. In the second step, the obtained optimal value is inserted on-line in a feedback control loop. The suggested optimized observer is used for two purposes: first it ensures an accurate estimation of state variables that are not physically measurable; despite the presence of disturbances and measurement noises; second it guarantees a stability of the considered system and the convergence of the estimation error. Results of simulated experimentations for robot manipulators are presented in order to demonstrate the performance and effectiveness of the proposed observer optimization.

本文针对输出为时间采样的机器人操纵器，提出了一种使用优化高增益的连续-离散时间观测器。这种方法的主要贡献在于通过使用一些元启发式算法，提高了与成本函数最小值相对应的高增益值。该观测器的特点是通过基于生物地理学的优化（BBO）算法、粒子群优化（PSO）方法和遗传算法（GA）优化最佳高增益。通过这项研究，证明了基于 BBO 算法的优化过程能获得最佳优化结果。BBO 是一种相对较新的受自然启发的优化算法，用于寻找优化问题的最佳和最优值。引入的方法分两步实施。第一步，利用 BBO 算法离线优化高增益。第二步，将获得的最优值在线插入反馈控制环路。建议的优化观测器有两个用途：首先，尽管存在干扰和测量噪声，它仍能确保对物理上不可测量的状态变量进行准确估计；其次，它能确保所考虑系统的稳定性和估计误差的收敛性。本文介绍了针对机器人机械手的模拟实验结果，以证明所提出的观测器优化方案的性能和有效性。

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Modified two-dimensional differential transform method for solving proportional delay partial differential equations 求解比例延迟偏微分方程的修正二维微分变换法

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2024-11-19 DOI: 10.1016/j.rico.2024.100499

Osama Ala’yed

In this study, we develop a modified version of the two-dimensional differential transform (TDDT) method for solving proportional delay partial differential equations (PDPDEs) that frequently arise in engineering and scientific models. This modification is achieved by integrating the TDDT method with the Laplace transform and the Padé approximant, thereby leveraging the strengths of each technique to improve overall performance. Theorems are provided in a general manner to cover various types of PDEs, with constant or variable coefficients. To validate the approach, we apply it to three test problems, demonstrating its effectiveness in extending the convergence domain of the traditional TDDT approach, reducing computational complexity, and yielding analytic solutions with fewer computational steps. Results indicate that the method is a viable alternative for addressing PDPDEs, especially in scenarios where traditional analytic solutions are challenging to obtain. This combination opens new avenues for efficiently solving complex delayed systems in engineering and science, potentially outperforming existing numerical and analytical techniques in both speed and reliability.

在本研究中，我们开发了一种改进版的二维微分变换（TDDT）方法，用于求解工程和科学模型中经常出现的比例延迟偏微分方程（PDPDE）。这种改进是通过将 TDDT 方法与拉普拉斯变换和帕代近似法相结合来实现的，从而利用每种技术的优势来提高整体性能。定理以通用方式提供，涵盖了具有常数或可变系数的各种类型的 PDE。为了验证该方法，我们将其应用于三个测试问题，证明它能有效扩展传统 TDDT 方法的收敛域，降低计算复杂性，并以更少的计算步骤获得解析解。结果表明，该方法是处理 PDPDEs 的可行替代方法，尤其是在传统分析解决方案难以获得的情况下。这种组合为高效解决工程和科学领域的复杂延迟系统开辟了新途径，有可能在速度和可靠性方面超越现有的数值和分析技术。

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