Search Results (55)

Background: Urban rail transit ensures efficient mobility in densely populated metropolitan areas. This study focuses on the Cairo Metro Network and addresses the Rolling Stock Rotation Planning Problem (RSRPP), aiming to improve operational efficiency and service quality. Methods: A Mixed-Integer Linear Programming (MILP) model is developed to integrate rolling stock rotation, deadhead routing, and maintenance scheduling. Two single-objective formulations are introduced to separately minimize denied passengers and the number of Electric Multiple Units (EMUs) used. To address scalability for larger instances, a Simulated Annealing (SA) metaheuristic is designed using a list-based solution representation and customized neighborhood operators that preserve feasibility. Results: Computational experiments based on real-world data validate the practical relevance of the model. The MILP achieves optimal solutions for small and medium-sized instances but becomes computationally infeasible for larger ones. In contrast, the SA algorithm consistently produces high-quality solutions with significantly reduced solve times. Conclusions: To the best of the authors’ knowledge, this is the first study to apply SA to the urban rail RSRPP while jointly integrating deadhead routing and maintenance scheduling. The proposed approach proves to be robust and scalable for large metro systems such as Cairo’s. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

In midcourse guidance, strong constraints and dual-channel control coupling pose major challenges for trajectory optimization. To address this, this paper proposes an optimal guidance method based on terminal relaxation and range convex programming. The study first derived a range-domain dynamics model with the angle of attack and bank angle as dual control inputs, augmented with path constraints including heat flux limitations, to formulate the midcourse guidance optimization problem. A terminal relaxation strategy was then proposed to mitigate numerical infeasibility induced by rigid terminal constraints, thereby guaranteeing the solvability of successive subproblems. Through the integration of affine variable transformations and successive linearization techniques, the original nonconvex problem was systematically converted into a second-order cone programming (SOCP) formulation, with theoretical equivalence between the relaxed and original problems established under well-justified assumptions. Furthermore, a heuristic initial trajectory generation scheme was devised, and the solution was obtained via a sequential convex programming (SCP) algorithm. Numerical simulation results demonstrated that the proposed method effectively satisfies strict path constraints, successfully generates feasible midcourse guidance trajectories, and exhibits strong computational efficiency and robustness. Additionally, a systematic comparison was conducted to evaluate the impact of different interpolation methods and discretization point quantities on algorithm performance. Full article

To better address the reconfiguration problem of distribution networks with distributed generation (DG), a dynamic reconfiguration model is developed that accounts for the time-varying characteristics of both load demand and DG output. First, an enhanced fuzzy C-means clustering method is proposed for load period partitioning, which integrates spatiotemporal load features and optimal network structure similarity to improve clustering accuracy. Second, an adaptive ordered loop-based feasibility judgment model is developed to filter infeasible and low-quality solutions based on operational constraints. Third, an improved Equilibrium Optimizer (IEO), integrating Tent chaotic initialization, elite sorting, and mutation-crossover strategies, is proposed for multi-objective optimization. The proposed framework is validated on IEEE 33- and 69-bus systems. In the IEEE 33-bus system, it achieves a 44.8% reduction in power losses and a 35.9% improvement in voltage deviation. In the IEEE 69-bus system, power loss is reduced by 40.1%, and voltage deviation by 40.5%, demonstrating the proposed method’s robustness, adaptability, and effectiveness across systems of varying scales. Full article

This paper proposes a multitasking-based genetic algorithm (MTGA-CMMO) to solve constrained multimodal multi-objective optimization problems (CMMOPs). In MTGA-CMMO, the main task is assisted by two auxiliary tasks to obtain all the feasible Pareto solution sets. The constraint boundaries of auxiliary task 1 are dynamically adjusted, facilitating the main task’s population in crossing infeasible regions early in the evolution and providing more evolutionary direction later in the evolution. Auxiliary task 2 can contribute to the exploitation ability of the main task. Meanwhile, a probability-based leader mating selection mechanism is devised to improve the global search capability of MTGA-CMMO. Additionally, three environmental selection strategies are designed to correspond to the different tasks in MTGA-CMMO. Extensive experimental verification demonstrates that MTGA-CMMO outperforms other comparative algorithms across multiple test instances and one practical application problem. Full article

This study investigates a green intermodal routing problem considering carbon tax regulation and a mixed (combined soft and hard) time window to improve cost- and time-effectiveness and promote carbon emission reduction in intermodal transportation. To enhance the feasibility of problem optimization, we model the uncertainty of both the carbon tax rate and the intermodal network capacity in the routing problem. By using interval fuzzy numbers to formulate the twofold uncertainty, an interval fuzzy linear optimization model is established to address the problem optimization, in which the optimization objective of the model is to minimize the total costs (consisting of transportation, time, and carbon emission costs). Furthermore, we conduct crisp processing of the proposed model to make the problem solvable, in which the optimization level, a parameter whose value is determined by the receiver before solving the problem, is introduced to represent the receiver’s attitude towards the reliability of transportation. We present a numerical experiment to verify the feasibility of the optimization model. The sensitivity analysis shows that the economics and environmental sustainability of the intermodal routing optimization conflict with its reliability. Improving the reliability of transportation increases both the total costs and the carbon emissions of the intermodal route. Furthermore, through comparison with deterministic modeling, the numerical experiment shows that modeling the twofold uncertainty can cover the different decision-making attitudes of the receiver, provide intermodal routes that are sensitive to the optimization level, enable flexible route decision-making, and avoid unreliable transportation. Through comparison with hard and soft time windows, the numerical experiment proves that the mixed time window is more applicable for problem optimization, since it can obtain the intermodal route that yields improved economics and environmental sustainability and simultaneously satisfies the receiver’s requirement for timeliness. Through comparison with the green intermodal route aiming at minimum carbon emissions, the numerical experiment indicates that carbon tax regulation under an interval fuzzy carbon tax rate is not feasible in all decision-making scenarios where the receivers have different attitudes regarding the reliability of transportation. When carbon tax regulation is infeasible, bi-objective optimization can provide Pareto solutions to balance the objectives of reduced costs and lowered carbon emissions. Finally, the numerical experiment reveals the influence of the release time of the transportation order at the origin and the stability of the interval fuzzy capacity on the routing optimization in the scenario in which the receiver prefers highly reliable transportation. Full article

Short-term optimal scheduling of cascade hydropower stations enhances their flexible regulation and power generation capabilities. However, nonlinear function relationships and multistage and hydraulic interdependencies present significant challenges, resulting in considerable solution errors, premature convergence, and high computational demands. This study proposes a progressive linear programming method that decomposes nonlinear functions to address these challenges. First, to accurately represent nonlinear functions and mitigate computational complexity, the entire feasible domain is partitioned into multiple contiguous subdomains in which nonconvex nonlinear functions within each subdomain can be equivalently replaced by linear relationships. Second, a progressive linear programming optimization algorithm is devised to prevent premature convergence, utilizing continuous subdomains rather than discrete points as state variables and incorporating the progressive optimality principle. Finally, to increase the solution efficiency, a dimensionality reduction strategy via the feasible domain state dynamic acquisition method is presented and optimized after excluding the infeasible states in each stage. The simulation of three cascade hydropower stations in a river basin in southwest China shows that the proposed method can achieve a superior peak regulation effect compared to the conventional mixed integer linear programming and progressive optimality algorithm. During the dry and wet seasons, the residual load peak–valley differences at the three stations are reduced by 612 MW and 521 MW compared to the MILP and 1889 MW and 2439 MW compared to the POA, which underscores the effectiveness of the method in optimizing the short-term scheduling of cascade hydropower stations. Full article

This study evaluates urban resilience to earthquakes in Daegu Metropolitan City, South Korea, by analyzing outdoor evacuation sites through a dual-axis matrix framework to provide feasible solutions for enhancing urban resilience. Evacuation capacity was assessed by use of resident and de facto population data, while Geographic Information System (GIS) network analysis identified evacuation-feasible and evacuation-infeasible areas. The matrix categorizes areas along two axes: capacity (x-axis) and evacuation-infeasible areas (y-axis), facilitating targeted improvement strategies. Findings reveal that only 54 of 139 census blocks possess sufficient capacity and no evacuation-infeasible areas. For areas with adequate capacity but extensive infeasible areas, redistributing evacuation sites is recommended to improve accessibility. Areas with limited capacity but no infeasible areas require additional outdoor evacuation sites to accommodate the population. In regions constrained by both capacity and accessibility, establishing new evacuation sites within infeasible areas is essential. For critically low-capacity areas without infeasible areas, multi-use spaces, such as disaster prevention parks, are desirable to address evacuation needs. Lastly, areas lacking both capacity and accessibility urgently require new evacuation sites concentrated in infeasible areas. By simplifying complex variables into a capacity–accessibility matrix, this study integrates population dynamics, spatial accessibility, and site capacity, offering implementable solutions for earthquake preparedness in densely populated urban settings. Additionally, this approach supports urban planning efforts to mitigate seismic damage and enhance urban sustainability. Full article

Navigating through complex flow fields, underwater vehicles often face insufficient thrust to traverse particularly strong current areas, necessitating consideration of the physical feasibility of paths during route planning. By constructing a flow field database through Computational Fluid Dynamics (CFD) simulations of the operational environment, we were able to analyze local uncertainties within the flow field. Our investigation into path planning using these flow field data has led to the proposal of a hierarchical planning strategy that integrates global sampling with local optimization, ensuring both completeness and optimality of the planner. Initially, we developed an improved global sampling algorithm derived from RRT to attain nearly optimal theoretical feasible solutions on a global scale. Subsequently, we implemented corrective measures using directed expansion to address locally infeasible sections. The algorithm’s efficacy was theoretically validated, and simulated results based on real flow field environments were provided. Full article

This study investigates an intermodal routing problem for transporting wood from a storage yard of the timber harvest area to a timber mill, in which the transfer nodes in the intermodal transportation network have multiple service time windows. To improve the environmental sustainability of timber transportation, a carbon tax policy is employed in the routing to reduce the carbon emissions. Uncertain information on the capacities and carbon emission factors of the transportation activities in the intermodal transportation network is modeled using interval fuzzy numbers to enhance the feasibility of the routing optimization in the actual timber transportation. Based on the above consideration, an interval fuzzy nonlinear optimization model is established to handle the specific routing problem. Model defuzzification and linearization are then conducted to obtain an equivalent formulation that is crisp and linear to make the global optimum solution attainable. A numerical experiment is conducted to verify the feasibility of the proposed model, and it reveals the influence of the optimization level and service time windows on the routing optimization, and it confirms that intermodal transportation is suitable for timber transportation. This experiment also analyzes the feasibility of a carbon tax policy in reducing the carbon emissions of timber transportation, and it finds that the performance of this policy is determined by the optimization level given by the timber mill and is not always feasible in all cases. For the case where a carbon tax policy is infeasible, this study proposes a bi-objective optimization that can use Pareto solutions to balance the economic and environmental objectives as an alternative. The bi-objective optimization further shows the relationship between lowering the transportation costs, reducing the carbon emissions, and enhancing the reliability on capacity and budget by improving the optimization level. The conclusions provide managerial insights that can help the timber mill and intermodal transportation operator organize cost-efficient, low-carbon, and reliable intermodal transportation for timber distribution, and support sustainable forest logistics. Full article

The key problem to solving constrained multi-objective optimization problems (CMOPs) is how to achieve a balance between objectives and constraints. Unfortunately, most existing methods for CMOPs still cannot achieve the above balance. To this end, this paper proposes an adaptive constraint relaxation-based evolutionary algorithm (ACREA) for CMOPs. ACREA adaptively relaxes the constraints according to the iteration information of population, whose purpose is to induce infeasible solutions to transform into feasible ones and thus improve the ability to explore the unknown regions. Completely ignoring constraints can cause the population to waste significant resources searching for infeasible solutions, while excessively satisfying constraints can trap the population in local optima. Therefore, balancing constraints and objectives is a crucial approach to improving algorithm performance. By appropriately relaxing the constraints, it induces infeasible solutions to be transformed into feasible ones, thus obtaining more information from infeasible solutions. At the same time, it also establishes an archive for the storage and update of solutions. In the archive update process, a diversity-based ranking is proposed to improve the convergence speed of the algorithm. In the selection process of the mating pool, common density selection metrics are incorporated to enable the algorithm to obtain higher-quality solutions. The experimental results show that the proposed ACREA algorithm not only achieved the best Inverse Generation Distance (IGD) value in 54.6% of the 44 benchmark test problems and the best Hyper Volume (HV) value in 50% of them, but also obtained the best results in seven out of nine real-world problems. Clearly, CP-TSEA outperforms its competitors. Full article

The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. Experimental results show that the proposed algorithm is highly competitive with several state-of-the-art competitors. Full article

As more network communication is encrypted to provide data privacy for users, attackers are focusing their attention on traffic analysis methods for side-channel attacks on user privacy. These attacks exploit patterns in particular features of communication flows such as interpacket timings and packet sizes. Unsupervised machine learning approaches, such as Hidden Markov Models (HMMs), can be trained on unlabelled data to estimate these flow attributes from an exposed packet flow, even one that is encrypted, so it is highly feasible for an eavesdropper to perform this attack. Traditional defences try to protect specific side channels by modifying the packet transmission for the flow, e.g., by adding redundant information (padding of packets or use of junk packets) and perturbing packet timings (e.g., artificially delaying packet transmission at the sender). Such defences incur significant overhead and impact application-level performance metrics, such as latency, throughput, end-to-end delay, and jitter. Furthermore, these mechanisms can be complex, often ineffective, and are not general solutions—a new profile must be created for every application, which is an infeasible expectation to place on software developers. We show that an approach exploiting multipath communication can be effective against HMM-based traffic analysis. After presenting the core analytical background, we demonstrate the efficacy of this approach with a number of diverse, simulated traffic flows. Based on the results, we define some simple design rules for software developers to adopt in order to exploit the mechanism we describe, including a critical examination of existing communication protocol behavior. Full article

Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM. Full article

The non-structural model of heat exchanger networks (HENs) offers a wide solution space for optimization due to the random matching of hot and cold streams. However, this stochastic matching can sometimes result in infeasible structures, leading to inefficient optimization. To address this issue, a tabu matching based on a heuristic algorithm for HENs is proposed. The proposed tabu-matching method involves three main steps: First, the critical temperature levels—high, medium, and low-temperature intervals—are determined based on the inlet and outlet temperatures of streams. Second, the number of nodes is set according to the temperature intervals. Third, the nodes of streams are flexibly matched within the tabu rules: the low-temperature interval of hot streams with the high-temperature interval of cold streams; the streams crossing cannot be matched. The results revealed that by incorporating the tabu rules and adjusting the number of nodes, the ratio of the feasible zone in the whole solution domain increases, and the calculation efficiency is enhanced. To evaluate the effectiveness of the method, three benchmark problems were studied. The obtained total annual costs (TACs) of these case studies exhibited a decrease of USD 4290/yr (case 1), USD 1435/yr (case 2), and USD 11,232/yr (case 3) compared to the best published results. The results demonstrate that the proposed tabu-matching heuristic algorithm is effective and robust. Full article