Optimization Approach Research Articles

Brain Re-Irradiation Or Chemotherapy: a phase II randomised trial of re-irradiation and chemotherapy in patients with recurrent glioblastoma (BRIOChe) – protocol for a multi-centre open-label randomised trial

IntroductionGlioblastoma (GBM) is the most common adult primary malignant brain tumour. The condition is incurable and, despite aggressive treatment at first presentation, almost all tumours recur after a median of...

Subregional Differentiated Safety Factors Design Based on Nonprobabilistic Structural Reliability

The structural safety factor is an essential parameter in aircraft design, representing the ratio of the design load to the operating load. Traditional design methods rely on subjective determination of safety factor values based on experience, lacking objectivity in quantifying uncertainty. However, with advancements in aircraft design technology and increasing competition in the commercial space market, new-generation hypersonic aircraft with complex load environments require a more optimal approach. Applying a uniform safety factor to each component within subregions of the aircraft leads to overly conservative results and impacts flight performance. To address this limitation, a design scheme that incorporates subregional, differentiated safety factors is necessary. This approach allows for better material utilization and ensures compliance with safety requirements. This paper utilizes reliability-based design optimization theory to consider uncertainty in structural systems. It establishes a mapping relationship between structural reliability and differentiated safety factors, providing safety under uncertainty while guaranteeing weight reduction. Additionally, this paper develops a subregional, differentiated safety factors distribution program to determine the safety factors of different subregions of the structure. Consequently, a refined subregional differentiated safety factors scheme that balances safety and economy is derived.

SISS-MCO: large scale sparsity-induced spot selection for fast and fully-automated robust multi-criteria optimisation of proton plans

Objective. Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation (MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix, and prostate tumours to a previously published method for automated robust MCO (IPBR-MCO, van de Water 2013). Approach. In both auto-planning approaches, the applied automated MCO of spot weights was performed with wish-list driven prioritised optimisation (Breedveld 2012). In SISS-MCO, spot weight MCO was applied once for every patient after sparsity-induced spot selection (SISS) for pre-selection of the most relevant spots from a large input set of candidate spots. IPBR-MCO had several iterations of spot re-sampling, each followed by MCO of the weights of the current spots. Main results. Compared to the published IPBR-MCO, the novel SISS-MCO resulted in similar or slightly superior plan quality. Optimisation times were reduced by a factor of 6 i.e. from 287 to 47 min. Numbers of spots and energy layers in the final plans were similar. Significance. The novel SISS-MCO automatically generated high-quality robust IMPT plans. Compared to a published algorithm for automated robust IMPT planning, optimisation times were reduced on average by a factor of 6. Moreover, SISS-MCO is a large scale approach; this enables optimisation of more complex wish-lists, and novel research opportunities in proton therapy.

One-shot procedures for efficient minimum compliance topology optimization

In this paper, a one-shot approach for minimum compliance topology optimization is investigated. In the convex case of variable thickness sheet optimization, an optimality criteria scheme with a single step of an iterative state solver converges to the same solutions as an accurate procedure. This remarkable behavior is explained by the crucial role of the geometric multigrid preconditioner that generates accurate design sensitivities on a coarse scale. When applied to SIMP-based topology optimization, a one-shot procedure yields designs with the same primary load-transferring features as an accurate procedure. The difference is in the absence and presence of thin features that have a relatively minor impact on the objective. Several practical remedies are suggested that can close the small performance gaps between the one-shot procedure and an accurate one. The solution scheme is directly applicable to large-scale problems executed on parallel computers, allowing for significant computational savings.

Improving the hydraulic performance of a high-speed submersible axial flow pump based on CFD technology

The hydraulic performance of a high-speed submersible axial flow pump is investigated to reduce its energy consumption. A more efficient and stable optimization method that combines parametric design, computational fluid dynamics, and a computer algorithm is proposed. The main aim is to broaden the high-efficiency operating zone, so the average efficiency under multiple conditions is optimized while considering rotor–stator matching. The design-of-experiments method and a radial-basis-function neural network are combined to form the optimization platform, and automatic optimization of the pump design is realized through repeated execution of design and simulation. The flow loss mechanism inside the pump is studied in depth via the entropy generation rate, and regression analysis shows that the pump efficiency is influenced mainly by the blade angles. After optimization, the target efficiency is increased by 8.34%, and the flow field distribution shows that the channel vortex and hydraulic loss are controlled effectively. Finally, the results are validated by experiment. The proposed optimization approach has advantages in saving manpower and obtaining globally optimal solutions.

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

The techno–environmental analysis of distributed energy resources in electrical distribution networks is a complex optimization task due to the non-convexities of its nonlinear programming formulation. This research employs convex optimization to address this issue while minimizing the expected carbon dioxide emissions and daily energy losses of a distribution grid via the optimal dispatch of battery energy storage units (BESUs) and renewable energy units (REUs). The exact non-convex model is approximated via semi-definite programming in the complex variable domain. The optimal Pareto front is constructed using a weighting-based optimization approach. Numerical results using an IEEE 69-bus grid confirm the effectiveness of our proposal when considering unitary and variable power factor operation for the BESUs and the REUs. All numerical simulations were carried out using MATLAB software (version 2022b), a convex disciplined tool (CVX), and the semi-definite programming solvers SEDEUMI and SDPT3.

A Blockchain-Based Cloud File Storage System Using Fuzzy-Based Hybrid-Flash Butterfly Optimization Approach for Storage Weight Reduction

A Blockchain-Based Cloud File Storage System Using Fuzzy-Based Hybrid-Flash Butterfly Optimization Approach for Storage Weight Reduction

Adapting to an Economic Crisis: The Market System vs Hierarchical Governance

This research investigates how exchange governance and the local market system influence a firm's adaptability to an economic crisis. This paper unveils a nuanced performance disparity by leveraging a rich dataset of manufacturing firms in Spain. While vertically integrated firms exhibit superior performance during periods of stability, they confront more significant setbacks in the aftermath of economic crises. This study demonstrates that the extent of this performance divergence is contingent upon the thickness of the local market system, supporting the hypothesis that vertically integrated firms derive reduced adaptive benefits from agglomeration economies. These findings shed light on the dynamic interrelationship between a firm's vertical scope and geographical context. They underscore the significance of a holistic assessment when determining the optimal approach to exchange governance. This assessment must evaluate the advantages and drawbacks of autonomous versus coordinated adaptation across economic cycles and geographies.

A Multi-Objective Optimization Approach for Low Volume Rural Road Maintenance Management Incorporating Socioeconomic Importance

A Multi-Objective Optimization Approach for Low Volume Rural Road Maintenance Management Incorporating Socioeconomic Importance

Resilient Cooperative Localization Based on Factor Graphs for Multirobot Systems

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.

Identifying general reaction conditions by bandit optimization.

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

Enhancing the high-temperature constitutive modeling of In706 superalloy using advanced artificial neural networks and attentive staged optimization algorithm

High-temperature constitutive models derived from the Arrhenius equation play a pivotal role in the hot-forging processes of nickel-based superalloys such as In706. However, existing models have limitations owing to insufficient data points, leading to the potential loss of vital information and the need for elaborate modeling methodologies. This study proposes an innovative optimization approach that leverages the attentive staged optimization algorithm (ASOA) and advanced artificial neural networks to enhance the accuracy and efficiency of In706 superalloy modeling. ASOA is a batch-based, multi-stage optimization algorithm inspired by the attention mechanism concept in deep learning. It allocates time and computational resources to the most deserving data for optimization and is well-suited for handling large volumes of data. In this paper, ASOA was employed to perform two rounds of fitting on the thermal simulation experimental data, resulting in the determination of the critical strain ϵc and identifying suitable Arrhenius equation material parameters for each set of deformation conditions. Subsequently, a full parameter responsive constitutive model was established for In706, capable of handling a comprehensive set of deformation conditions and providing a more detailed representation of the high-temperature behavior of the In706 alloy. In developing the FPR model, we designed a fully connected neural network and employed a cosine annealing dual-error dynamic adjust function to address error amplification during training. The cosine annealing learning rate scheduling strategy with amplitude attenuation was also implemented to dynamically adjust the learning rate. As a result, the model developed using the proposed approach outperforms traditional models and shows remarkable potential for the precise and efficient optimization of In706 alloy. The high performance of the final model showcases the prospect of our approach in future development and optimization of superalloys.

Optimal and total controllability approach of non-instantaneous Hilfer fractional derivative with integral boundary condition.

The focus of this work is on the absolute controllability of Hilfer impulsive non-instantaneous neutral derivative (HINND) with integral boundary condition of any order. Total controllability refers to the system's ability to be controlled during the impulse time. Kuratowski measure and semigroup theory in Banach space yield the results. Furthermore, we talked about optimal controllability in conjunction with appropriate limitations. Our established outcomes are described using an example.

High Aspect Ratio Composite Wings: Geometrically Nonlinear Aeroelasticity, Multi-Disciplinary Design Optimization, Manufacturing, and Experimental Testing

In the field of aerospace engineering, the design and manufacturing of high aspect ratio composite wings has become a focal point of innovation and efficiency. These long, slender wings, constructed with advanced materials such as carbon fiber and employing efficient manufacturing methods such as vacuum bagging, hold the promise of significantly lighter aircraft, reduced fuel consumption, and enhanced overall performance. However, to fully realize these benefits, it is imperative to address a multitude of structural and aeroelastic constraints. This research presents a novel aeroelastically tailored Multi-objective, Multi-disciplinary Design Optimization (MMDO) approach that seamlessly integrates numerical optimization techniques to minimize weight and ensure structural integrity. The optimized wing configuration is then manufactured, and a Ground Vibration Test (GVT) and static deflection analysis using the Digital Image Correlation (DIC) system are used to validate and correlate with the numerical model. Within the fully automated in-house Nonlinear Aeroelastic Simulation Software (NAS2) package (version v1.0), the integration of analytical tools offers a robust numerical approach for enhancing aeroelastic and structural performance in the design of composite wings. Nonlinear aeroelastic analyses and tailoring are included, and a population-based stochastic optimization is used to determine the optimum design within NAS2. These analytical tools contribute to a comprehensive and efficient methodology for designing composite wings with improved aeroelastic and structural characteristics. This comprehensive methodology aims to produce composite wings that not only meet rigorous safety and performance standards but also drive cost-efficiency in the aerospace industry. Through this multidisciplinary approach, the authors seek to underscore the pivotal role of tailoring aeroelastic solutions in the advanced design and manufacturing of high aspect ratio composite wings, thereby contributing to the continued evolution of aerospace technology.

An optimization approach control of EV solar charging system with step-up DC–DC converter

An optimization approach control of EV solar charging system with step-up DC–DC converter

Multi-objective optimisation of a logistics area in the context of factory layout planning

ABSTRACT The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches.

Market design for ancillary service provisions of inertia and frequency response via virtual power plants: A non-convex bi-level optimisation approach

The Great Britain (GB) government is paving the path to decarbonisation by actively promoting the integration of wind power into its generation mix. This sharp transition to renewable energy, however, introduces specific challenges. The characteristics of non-synchronous wind turbines have the potential to impact grid stability and frequency security due to the reduction in system inertia. In response to these challenges, the deployment of virtual power plants (VPPs) is envisioned in deregulated power systems, which allow for the aggregation and coordinated control of diverse distributed energy resources, enhancing grid flexibility, reliability, and efficiency. Moreover, VPPs offer the provision of system inertia and frequency response services at the national level. This study drops this assumption and leverages the localised flexibility inherent in VPPs to meet the ancillary service requirements of the low-carbon power system. The formulated problem is constructed as a non-convex bi-level optimisation problem, where the upper-level problem represents the operation of a frequency-constrained unit commitment model, and the lower-level problem embodies the energy dispatches and frequency responses of a group of VPPs, guided by the dual price signals of cleared energy and ancillary services. To address the non-convex nature of the unit commitment problem, a two-fold approach is employed. First, binary commitment status decision variables are relaxed to continuous versions. Subsequently, the duality gap between the original non-convex unit commitment problem and its relaxed dual form is minimised, yielding a solution closely approximating the optimal solution of the original problem. Second, the relaxed bi-level optimisation problem is transformed into a single-level mathematical programs with equilibrium constraints by replacing the lower-level VPP problems with their equivalent Karush-Kuhn–Tucker optimality conditions. The case studies conducted in this work encompass a comprehensive scope. Initially, the study evaluates the effectiveness of VPPs in delivering ancillary services within the projected context of the GB power system for 2030. The findings reveal a substantial 28.28% decrease in system operation costs when compared to the benchmark case lacking VPP flexibility. Additionally, the integration of VPPs leads to significantly lower price signals for inertia, primary frequency response, and enhanced frequency response, reduced by 81.21%, 79.13%, and 86.27%, respectively, in contrast to the benchmark case. The analysis then delves into a sensitivity exploration, investigating the inherent flexibility of VPPs. The profit of local VPPs is expected to increase with VPP flexibility. Finally, the study illustrates how VPPs can adapt to and derive benefits from an increasing level of wind power penetration by providing a higher amount of frequency response.

Advances in making Mn oxide‐coated sands

AbstractManganese (Mn) oxide‐coated sand has been suggested as an amendment for scrubbing metals in water filtration beds and also as a less concentrated medium for uniformly amending soils with Mn oxides in mesocosm scale studies. Earlier work at the lab bench scale, using potassium permanganate (KMnO4) solutions that were reduced with sodium (Na) lactate, resulted in sands coated with about 0.13% Mn. The goal of this project was to increase the amount of Mn oxide that could be coated on sand to make it a more useful amendment and also to attempt to scale up the procedure to produce larger (kg) quantities of coated sand. Titration experiments examined the effects of (1) varying the molar ratio of Na lactate to KMnO4, (2) varying the rate at which the titration was accomplished, and (3) varying the concentration (molarity) of the original KMnO4 solution. The results of this work led to an optimal approach utilizing 0.32 M KMnO4 solution that was titrated to a final lactate:permanganate ratio of ∼1.1 with 10% of the lactate being added every 10 min while the suspension was being stirred. The proportion of sand to an initial solution was also increased 5–20 fold to between 50 and 200 g per 100 mL of solution. Applying this method and using a large 20‐ to 30‐L reaction vessel yields sands coated with up to 0.7% Mn in batches 5–10 kg is size, which could be useful as an amendment in mesocosm scale studies, or as a component of treatment filter beds. The examination of various size fractions of the coated sands demonstrated that more Mn was coated on finer sand fractions, which appears to be a function of the particle surface area available for the coating of Mn oxides, and at a rate of 0.3–0.5 µg Mn mm−2 of the particle surface.