Search Results (173)

To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed through hydraulic fracturing by combining the XGBoost, multi-objective particle swarm optimization (MOPSO), and numerical models. XGBoost was used to generate a surrogate model to approximate the physical model, and the numerical model was used to generate a dataset for XGBoost. MOPSO is regarded as an optimal technology to deal with the conflict between multi-objective functions in inverse analysis. On comparing the results between the actual geomechanical properties and those obtained by using traditional inverse analysis, the proposed framework accurately characterizes the geomechanical parameters of reservoirs obtained through hydraulic fracturing. This provides a feasible, scientific, and promising way to characterize reservoir formation in petroleum engineering, as well as a reference for other fields of engineering. Full article

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households. Full article

Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the utilization of the storage capacity of UAVs without a proper UAV coordination mechanism. This work proposes a multi-UAV-enabled caching socially aware network (SAN) where UAVs can switch roles by adjusting the social attributes, effectively enhancing data interaction within the UAVs. The proposed network breaks down communication barriers at the UAV layer and integrates the collective storage resources by incorporating social awareness mechanisms to mitigate these imbalances. Furthermore, we formulate a multi-objective optimization problem (MOOP) with the objectives of maximizing both the diversity of cached content and the total request probability (RP) of the network, while employing a multi-objective particle swarm optimization (MOPSO) algorithm with a mutation strategy to approximate the Pareto front. Finally, the impact of key parameters on the Pareto front is analyzed under various scenarios. Simulation results validate the benefits of leveraging social attributes for resource allocation and demonstrate the effectiveness and convergence of the proposed algorithm for the multi-UAV caching strategy. Full article

Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn. Full article

This paper aims to find an efficient optimization algorithm to bring down the cost function without compromising the stability of the system and respect the operational constraints of the Hybrid Renewable Energy System. To accomplish this, MATLAB simulations were carried out using three optimization techniques: Grey Wolf Optimization (GWO), Teaching–Learning-Based Optimization (TLBO), and Multi-Objective Particle Swarm Optimization (MOPSO). The study compared their outcomes to identify which method yielded the most effective performance. The research included a statistical analysis to evaluate how consistently and stably each optimization method performed. The analysis revealed optimal values for the output power of photovoltaic systems (PVs), wind turbines (WTs), diesel generator capacity (DGs), and battery storage (BS). A one-year period was used to confirm the optimized configuration through the analysis of capital investment and fuel consumption. Among the three methods, GWO achieved the best fitness value of 0.24593 with an LPSP of 0.12528, indicating high system reliability. MOPSO exhibited the fastest convergence behaviour. TLBO yielded the lowest Net Present Cost (NPC) of 213,440 and a Cost of Energy (COE) of 1.91446/kW, though with a comparatively higher fitness value of 0.26628. The analysis suggests that GWO is suitable for applications requiring high reliability, TLBO is preferable for cost-sensitive solutions, and MOPSO is advantageous for obtaining quick, approximate results. Full article

To study the dynamic characteristics of the fluid-filled ship piping system with flanges and to optimize the design, and based on the transfer matrix methods (TMMs), this paper proposes two modeling methods for flat-welded flanges and weld-neck flanges. Method 1 employs a lumped mass equivalent flange. Method 2, based on the finite element and analogy ideas, equates the flange to pipe sections with a larger wall thickness. By comparing with the finite element method (FEM) results, it is found that for both flat-weld flanges and weld-neck flanges, the accuracy of Method 2 proposed in this paper is superior to that of Method 1. Meanwhile, experimental verification is carried out, and the experimental results are generally consistent with those obtained using Method 2. Furthermore, the multi-objective particle swarm optimization (MOPSO) algorithm is further introduced for the dynamic design of a branch pipeline system. The goal is to avoid resonance by adjusting the natural frequency of the system. Through the comparison of the FEM results, it has been confirmed that this optimization method is both efficient and accurate in optimizing the natural frequency. The method proposed in this paper has a specific reference value for engineering practice. Full article

With the increasing penetration of distributed generation (DG), integrated main-distribution networks (IMDNs) face challenges in rapidly and effectively performing comprehensive operational risk assessments under multiple uncertainties. Thereby, using the traditional hierarchical economic scheduling method makes it difficult to accurately find the optimal scheduling operating point. To address this problem, this paper proposes a multi-objective dispatch decision-making optimization model for the IMDN with static security region (SSR) constraints. Firstly, the non-sequential Monte Carlo sampling is employed to generate diverse operational scenarios, and then the key risk characteristics are extracted to construct the risk assessment index system for the transmission and distribution grid, respectively. Secondly, a hyperplane model of the SSR is developed for the IMDN based on alternating current power flow equations and line current constraints. Thirdly, a risk assessment matrix is constructed through optimal power flow calculations across multiple load levels, with the index weights determined via principal component analysis (PCA). Subsequently, a scheduling optimization model is formulated to minimize both the system generation costs and the comprehensive risk, where the adaptive grid density-improved multi-objective particle swarm optimization (AG-MOPSO) algorithm is employed to efficiently generate Pareto-optimal operating point solutions. A membership matrix of the solution set is then established using fuzzy comprehensive evaluation to identify the optimal compromised operating point for dispatch decision support. Finally, the effectiveness and superiority of the proposed method are validated using an integrated IEEE 9-bus and IEEE 33-bus test system. Full article

Cable force distribution in cable-stayed bridges critically impacts structural safety and efficiency, yet traditional optimization methods struggle with unconventional designs due to nonlinear mechanics and computational inefficiency. This study proposes a hybrid approach combining Response Surface Methodology (RSM) and Multi-Objective Particle Swarm Optimization (MOPSO) to overcome these challenges. RSM constructs surrogate models for strain energy and mid-span displacement, reducing reliance on finite element analysis, while MOPSO optimizes Pareto solution sets for rapid cable force adjustment. Validated through an engineering case, the method reduces the main girder’s max bending moment by 8.7%, mid-span displacement by 31.2%, and strain energy by 7.1%, improving stiffness and mitigating stress concentrations. The response surface model demonstrates prediction errors of 0.35% for strain energy and 5.1% for maximum vertical mid-span deflection. By synergizing explicit modeling with intelligent algorithms, this methodology effectively resolves the longstanding efficiency–accuracy trade-off in cable force optimization for cable-stayed bridges. It achieves over 80% reduction in computational costs while enhancing critical structural performance metrics. Engineers are thereby equipped with a rapid and reliable optimization framework for geometrically complex cable-stayed bridges, delivering significant improvements in structural safety and construction feasibility. Ultimately, this approach establishes both theoretical substantiation and practical engineering benchmarks for designing non-conventional cable-stayed bridge configurations. Full article

Energy is a critical resource, and its optimization is central to sustainable building design. Occupant comfort, significantly influenced by factors, including mean radiant temperature (MRT), alongside air temperature, velocity, and humidity, is another key consideration. This paper introduces a hybrid crow search optimization (CSA) and penguin search optimization algorithm (PeSOA), termed (HCRPN), designed to simultaneously optimize building energy consumption and achieve MRT levels conducive to thermal comfort by adjusting HVAC system parameters. We first validate HCRPN using ZDT-1 and Shaffer N1 multi-objective benchmarks. Subsequently, we employ EnergyPlus simulations, utilizing a single-objective Particle Swarm Optimization (PSO) for initial parameter analysis to generate a dataset. Following correlation analyses to understand parameter relationships, we implement our hybrid multi-objective approach. Comparative evaluations against state-of-the-art algorithms, including MoPso, NSGA-II, hybrid Nsga2/MOEAD, and Mo-CSA, validated the effectiveness of HCRPN. Our findings demonstrate an average 7% reduction in energy consumption and a 3% improvement in MRT-based comfort relative to existing methods. While seemingly small, even minor enhancements in MRT can have a noticeable positive impact on well-being, particularly in large, high-occupancy buildings. Full article

During centralizer hydroforming, internal pressure and axial feed critically influence the forming outcome. Insufficient feed causes excessive thinning and cracking, while excessive feed causes thickening and wrinkling. Achieving uniform wall thickness necessitates careful design of the pressure and feed curves. Using max/min wall thickness as objectives and key control points on these curves as variables, the study integrated Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Neighborhood Cultivation Genetic Algorithm (NCGA), and Archive-based Micro Genetic Algorithm (AMGA) with LS-DYNA to automatically optimize loading paths. The results demonstrate the following: ① NSGA-II, NCGA, and AMGA successfully generated optimized paths; ② NSGA-II and AMGA produced larger sets of higher-quality Pareto solutions; ③ AMGA required more iterations for satisfactory Pareto sets; ④ MOPSO exhibited a tendency towards premature convergence, yielding inferior results; ⑤ Multi-objective optimization efficiently generated diverse Pareto solutions, expanding the design space for process design. Full article

This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective particle swarm optimization (MOPSO), the study simultaneously optimizes three key objectives: economic performance (maximizing net present value, NPV), system reliability (minimizing loss of power supply probability, LPSP), and operational efficiency (reducing curtailment). The optimized HPP (283 MW wind, 20 MW solar, and 500 MWh BESS) yields an NPV of $165.2 million, a levelized cost of energy (LCOE) of $0.065/kWh, an internal rate of return (IRR) of 10.24%, and a 9.24-year payback, demonstrating financial viability. Operational efficiency is maintained with <4% curtailment and 8.26% LPSP. Key findings show that grid imports improve reliability (LPSP drops to 1.89%) but reduce economic returns; higher wind speeds (11.6 m/s) allow 27% smaller designs with 54.6% capacity factors; and tax credits (30%) are crucial for viability at low PPA rates (≤$0.07/kWh). Validation via Multi-Objective Genetic Algorithm (MOGA) confirms robustness. The study improves hybrid power plant design by combining weather predictions, policy changes, and optimizing three goals, providing a flexible renewable energy option for reducing carbon emissions. Full article

This paper presents an occupant-centric load optimization framework for Smart Green Townhouses (SGTs). A hybrid Long Short-Term Memory and Convolutional Neural Network (LSTM-CNN) model is combined with real-time Internet of Things (IoT) data to predict and optimize energy usage based on occupant behavior and environmental conditions. Multi-Objective Particle Swarm Optimization (MOPSO) is applied to balance energy efficiency, cost reduction, and occupant comfort. This approach enables intelligent control of HVAC systems, lighting, and appliances. The proposed framework is shown to significantly reduce load demand, peak consumption, costs, and carbon emissions while improving thermal comfort and lighting adequacy. These results highlight the potential to provide adaptive solutions for sustainable residential energy management. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

This study focuses on optimizing two tandem columns to separate ethylbenzene and styrene. A steady-state model is developed to minimize total energy consumption (TEC) and total annualized cost (TAC) by optimizing the reflux flow rates. An integrated dynamic model is created using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. This model is designed to account for transitions in operating conditions and to identify optimal dynamic strategies for adjusting operations to maintain optimal performance. The optimization considers factors such as fluctuation amplitude, the number of fluctuations, and fluctuation duration. The aim is to reduce fluctuation amplitudes while ensuring higher energy efficiency and stable operation. The results reveal that the optimal reflux flow rates are 41,152.2 kg/h and 1012.7 kg/h, leading to reductions in TEC and TAC by 16.7% and 17.4%, respectively. Compared with the industry standard level, the energy consumption has decreased by 11.25%. Against the backdrop of increasingly strict global carbon emission control, the market competitiveness of ethylbenzene/styrene production has been significantly enhanced. The variable-step adjustment method requires less time to reach a stable state, while the equal-step fluctuation method provides more stability. The Pareto solution set derived from the two optimization techniques can be used to select the most suitable adjustment strategy, ensuring a fast and smooth transition. Full article

Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs. When distributed geographically, GDCs face considerable challenges due to spatial variations in renewable energy availability, electricity pricing, and bandwidth costs. This paper addresses the joint optimization of operational cost and service quality for delay-sensitive applications scheduled across distributed green data centers (GDDCs). We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. To solve this, we propose an Adaptive Firefly-Based Bi-Objective Optimization (AFBO) algorithm that introduces multiple adaptive mechanisms to improve convergence and diversity. The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). The results show that AFBO achieves up to 64-fold reductions in operational cost and produces an extremely low ATLP value ($1.875\times {10}^{-7}$) that is nearly two orders of magnitude lower than SBDE and MOFA and several orders better than MOPSO. These findings confirm AFBO’s superior capability to balance energy cost savings and Quality of Service (QoS), outperforming existing methods in both solution quality and convergence speed. Full article