Multi Optimization Research Articles

Bulkheads’ Position Optimisation in the Concept Design of Ships under Deterministic Rules

The position of the transverse bulkheads is the most important aspect in determining the internal subdivision of the ship and has a strong impact on the general arrangement, weight distribution and capacity of the ship. Nowadays, deterministic rules still apply to various types of ships such as gas carriers, naval ships, icebreakers, etc. For these vessels a new floodable length can be defined as the extent of the ship that can be flooded, still assuring compliance with the damage stability criteria. The main objective of this paper is using the floodable lengths to optimize the position of bulkheads. The proposed methodology maximises the margin between the floodable length and the actual flooded length, which consists of two lost contiguous compartments. This method, applicable in the framework of multi-attribute decision-making techniques for ship concept design, allows identification of the minimum number of bulkheads a ship requires, quantification, and maximisation of the safety margin for compliance with deterministic damage stability criteria. This margin ensures maximum flexibility for changes that may be required in the next design phases. The proposed method, based on a multi-stage optimization, is tested on a compressed natural gas carrier to define the minimum number and position of the transverse bulkheads dividing the cargo holds.

A flexible calibration method with multi-stage optimization for the axial error of mobile mapping systems

Mobile mapping systems, which are equipped with a laser scanner, panoramic camera, and inertial navigation system, can flexibly and efficiently obtain precision point clouds. However, these systems require re-installation for each mission, which causes displacement errors in the instruments, resulting in discrepancies between multiple collections of data. The existing calibration methods usually require additional control targets settled in a managed field survey. Furthermore, the point clouds obtained with mobile mapping systems do not follow the common assumption of rigid transformation. To alleviate the above problems, we propose a flexible in-situ calibration method that only requires data collected in a round-trip survey pattern and conducts a self-calibration of the placement parameters with meticulously designed exact point matching. Specifically, because the accuracy of the existing feature-based registration method is not sufficient to acquire the exact correspondences of point clouds and to prune the outliers in correspondence searching. We propose novel pruning and refining approaches to obtain exact point-to-point correspondences between the round-trip point clouds. From systematic analyses of the coupling between different factors, we then construct a simplified error model that takes into account the a priori rotation- and translation-weighted observations. Finally, to alleviate the correlation effects between different parameters, we decouple the least-squares solver into several stages and obtain robust self-calibration parameters. Surveying experiments show that the flexible calibration method proposed in this paper is both effective and feasible in improving the accuracy of point clouds in overlapping areas, and can reduce the inconsistency of the calibrated point clouds by 37.02%.

Performance analysis and multi-objective optimization of bionic dendritic furcal energy-absorbing structures for trains

Rail vehicles offer the characteristics of large carrying capacity and high running speed. Train accidents usually result in substantial personal casualties and economic losses. Energy-absorbing structures can efficiently and quickly absorb and disperse the energy generated during collisions. In this paper, the performances with different cross-sections and partings of the bionic dendritic furcal structure (BDFS) inspired by the furcal branch and bifurcation structure of neurons are analyzed and compared by numerical simulation. The simplified super folding element theory and experiments verify that the finite element models are effective. A hybrid multi-stage optimization decision system, i.e., multi-criteria decision-making (MCDM)- multi-objective optimization (MOP)- MCDM, which integrates the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, the multiple objective particle swarm optimization-crowding distance (MOPSO-CD) evolutionary algorithm and repetitive VIKOR method, is proposed to find the optimal structure and obtain the optimal parameter alternative of the structure. Comparisons and analyses of experiments and simulations are carried out to verify the effectiveness of the proposed method. The results show that BDFS with six furcal ribs and 3rd-order parting display the best comprehensive crashworthiness under the working condition of 10 m/s specified in EN15227 standard. When the single target requirement is considered, specific energy absorption (SEA) can be increased by 16.18%, peak crushing force (PCF) can be decreased by 67.07%, and undulation of the load-carrying capacity (ULC) can be decreased by 14.02%. This study provides an effective tool for the analysis and optimization of energy-absorbing structures.

Research on Partner Selection of High-Speed Railway Dynamic Logistics Alliance Based on the Dynamic Programming Model

Traditional railway transportation can no longer meet people’s demand for logistics services. This paper takes advantage of high speed to propose a high-speed railway dynamic logistics alliance based on a cloud platform to make up for the lack of transport capacity at both ends of the high-speed railway logistics trunk line. Selecting partners is crucial to the high-speed rail logistics alliance. This paper uses the methods of multiobjective fuzzy optimization and dynamic programming to conduct multistage optimization of high-speed railway dynamic logistics alliance partners. When the market demand changes, in order to optimize the overall interests of the alliance, this paper uses the efficiency profit field method to achieve the dynamic selection of alliance partners or potential partners. The case study shows that the establishment of the high-speed railway dynamic logistics alliance can optimize the interests of the members of the alliance, verify the effectiveness of the method, and provide a reference for the better development of high-speed railway logistics.

On the Value of Dynamism in Transit Networks

The rise of on-demand mobility technologies over the past decade has sparked interest in the integration of traditional transit and on-demand systems. One of the main reasons behind this is the potential to address a fundamental trade-off in transit: the ridership versus coverage dilemma. However, unlike purely fixed systems or purely on-demand systems, integrated systems are not well understood; their planning and operational problems are significantly more challenging, and their broader implications are the source of a heated debate. Motivated by this debate, we introduce the dynamicity gap, a general concept that quantifies the attainable benefit of allowing (but not requiring) dynamic components in the response strategy to a multistage optimization problem. Although computing the dynamicity gap exactly may be intractable, we develop an analytical framework with which to approximate it as a function of problem input parameters. The framework allows us to certify the value of dynamism (i.e., a dynamicity gap greater than one) for certain combinations of problem input parameters. We showcase our approach with two sets of computational experiments, from which we gain both qualitative and quantitative insights about the settings in which the integration of transit and on-demand systems may certifiably be a worthwhile investment.Funding: This work was partially supported by the National Science Foundation [Grants DMS-1839346 and CNS-1952011]. Part of this research was performed while the authors were visiting the Institute for Pure and Applied Mathematics, which is supported by the National Science Foundation [Grant DMS-1925919].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1193 .

Data-Driven Adaptive Prediction of Cloud Resource Usage

Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.

Weakly Supervised Semantic Segmentation Via Progressive Patch Learning

Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standard classification network. In this paper, a novel ‘`Progressive Patch Learning’' approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models. ‘`Patch Learning’' destructs the feature maps into patches and independently processes each local patch in parallel before the final aggregation. Such a mechanism enforces the network to find weak information from the scattered discriminative local parts, achieving enhanced local details sensitivity. ‘`Progressive Patch Learning’' further extends the feature destruction and patch learning to multi-level granularities in a progressive manner. Cooperating with a multi-stage optimization strategy, such a ‘`Progressive Patch Learning’' mechanism implicitly provides the model with the feature extraction ability across different locality-granularities. As an alternative to the implicit multi-granularity progressive fusion approach, we additionally propose an explicit method to simultaneously fuse features from different granularites in a single model, further enhancing the CAM quality on the full object coverage. Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset <i>e.g.</i>, with 69.6% mIoU on the <i>test</i> set), which surpasses most existing weakly supervised semantic segmentation methods.

다단계 최적화 프로세스를 활용한 축류 펌프 임펠러 설계

In this paper, design optimization and suggestion of design improvement of the axial-flow pump impeller are investigated using the multi-stage optimization process. it is constructed by screening, metamodeling, design optimization, and analysis of design optimization results. Throughout the process, Commercial PIDO software called PIAnO and AIDesigner was used. For design simplification and efficiency refinement of the design optimization process, screening of design variables was applied, filtered 4 variables were used to construct metamodels of performance characteristics. Metamodels with the least error were polynomial regression for Efficiency and Kriging for the total head, and they were used on design optimization of the axial pump impeller. The method of design optimization was PQRSM selected by AIDesigner. As a result, the axial-flow pump impeller was satisfied its required total head with improved efficiency. Using contribution analysis and trade-off analysis, it can be improved when shroud length would be increased larger than its own upper limitation.

A graphical approach for filter pruning by exploring the similarity relation between feature maps

The vast majority of repetitive pruning and retraining techniques used on CNNs require multi-stage optimization, which undermines the potential computing savings from pruning. The similarity relationship between the output feature maps of the filters is first observed and represented as a graphical model in this paper in order to solve this issue. Vertex redundancy in a graph is determined using the degree of a vertex and the weight of its edges. Pruning is then carried out “one-shot” by removing the filters associated with redundant feature maps from each layer. In order to preserve performance, the network must finally be retrained. The CIFAR-10 dataset is identified using the classifier produced by applying this proposed methodology to several deep learning models. In this instance, both the parameters reduction and FLOPs reduction of the trimmed model are significantly improved. This method, tested on VGG-16, reduces parameters by 90.8% and FLOPs by 60.0% while sacrificing little accuracy (=0.16%) in comparison to the baseline. The results based on ResNet-110 show that applying the proposed approach can reduce FLOPs and parameters by 70.4% while preserving baseline accuracy with a loss of just 0.05%.

Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet

Large-scale adoption of Robotaxi requires a comprehensive charging infrastructure as a guarantee, but the problems of insufficient capacity, low utilization, and unreasonable deployment of electric vehicle charging stations (EVCSs) are still common. In this context, this study proposed a multi-stage optimization strategy consisting of fleet sizing, charging demand simulation, model construction and solution to achieve the location and capacity optimization of EVCSs for the electric Robotaxi fleet. In stage one, a vehicle shareable network model based on graph theory was developed to determine the minimum Robotaxi fleet size required to adequately meet user travel demand, which was solved using the Hopcroft-Karp algorithm. In stage two, the specific spatio-temporal distribution of fleet charging demand was obtained by Monte Carlo simulation, considering the decision-making characteristics of Robotaxi operation and charging process. In stage three, a charging station location and capacity optimization model was established with the objective of minimizing the comprehensive costs, and an improved particle swarm optimization algorithm applying genetic operators to improve the population diversity was proposed. Finally, the effectiveness of the proposed model and algorithm was analyzed and discussed based on a case study using the real passenger order and geographic data from the city of Chengdu, China.

Multi-Stage Optimization of LHTESS by utilization of Y-shaped Fin in a rectangular enclosure

Rectangular cavities are efficient tools for latent energy storage which have versatile applications in industry. A great effort has been dedicated to optimize energy storage and melting performance of PCMs in these systems. In this work, a Y-shaped fin has been adopted in a rectangular enclosure filled with Lauric acid as PCM and has been optimized in three distinct stages to find the optimum case. Different parameters have been adjusted to enhance the melting of PCM which include length of base of the fins, length of each of the branches of fins separately and the angle between two branches. The best case selected at each stage of optimization has been selected for further enhancement in the next stage. Enthalpy-porosity approach was used to simulate the melting process. It was concluded that case 3 at optimization stage 1 and case 6 in optimization stage 2 outperform other cases as they can better improve the natural convection generated in the enclosure. Also case 8 with angle of 120 ° has the best performance among all cases with 23.3% faster melting time in comparison with the base case.

Multistage optimization of a petroleum production system with material balance model

In this paper, we propose a mathematical formulation for the optimal management over time of an oil production network as a multistage optimization problem. The proposed model differs from common practice where the reservoir of the oil production network is approximated by decline curves or by black-box simulators. We model the reservoir as a controlled (non-linear) dynamical system by using material balance equations, under the standard assumptions that the fluids follow a black-oil model and that the reservoir has a tank-like behavior. The state of the dynamical system has five dimensions: the total volume of respectively oil, gas, and water in the reservoir; the total pore volume; and the reservoir pressure. We use a dynamic programming algorithm to numerically solve the multistage optimization problem on two specific instances of the general optimization problem where the state dimension can be reduced from dimension five to dimension one or two. More precisely, the first numerical application consists in optimizing the production of a dry gas reservoir which is subdivided in two tanks and which leads to a two-dimensional state (one dimension per tank), whereas the second numerical application tackles an oil reservoir with water injection which leads to a two-dimensional state. The two applications illustrate that our approach handles interconnected tanks (in the dry gas case) and that our approach allows optimization beyond first recovery of oil (in the oil with water injection case). We also provide numerical and theoretical comparisons with decline curves in the dry gas application.

Updating multi-fidelity structural dynamic models for flexible wings with feed-forward neural network

In multidisciplinary design optimization of aerospace structures (e.g., a flexible wing), it may be convenient and practical to break such a complex problem into multi-fidelity, multi-stage design problems. Structural model updating is needed in multi-fidelity, multi-stage optimizations to ensure the consistency of models with different fidelity. However, due to the inequality in structural parameters, there exists a fundamental difficulty in the model updating from a lower fidelity model to a higher fidelity model. In this paper, a feed-forward neural network is applied to determine the structural dynamic characteristics of a higher fidelity model based upon a lower fidelity model. The feasibility of this approach is demonstrated by updating beam-like wings to a thin shell-based model and a one-cell wing box model, respectively. The quality and accuracy of model updating using the proposed method are also discussed regarding the neural network structure and sample size.

Multistage optimization of growth and physiological condition of brackish green microalgae with the use of natural waters

We investigated the achievable maxima of optimisation of both growth and physiological condition of the preselected, Baltic green microalgae strains Chlorella vulgaris and Monoraphidium convolutum. The optimisation was divided into 3 consecutive stages of 8 days batch cultures, each focused on the effect of different set of factors on microalgae growth. The conditions identified as the most optimal served as a growth rate reference for the subsequent stage. We monitored the algal growth results spectrophotometrically (absorbance), gravimetrically (dry biomass DW) and biochemically (% protein, carbohydrates, lipids of DW). From the latter, it was possible to infer the physiological state of the microalgae and thus the presence or absence of a cellular stress factor. Stage 1 concerned the influence of basic culture parameters such as water salinity, medium type, and the use of natural waters in place of artificial media (i.e., fresh/brackish, environmental/artificial); stage 2 - the impact of the nitrogen source and its concentration (organic/inorganic, diluted/concentrated) and carbonates addition. Finally, stage 3 concentrated on the influence of the photoperiod and light spectral composition. The results show that among the different approaches to optimize algal growth, the most noticeable improvement was gained using environmental waters in place of artificial ones as a base for preparing the culture medium. Culture on natural, fresh river water yielded 61.1% and 57.4% higher biomass DW/L for C.vulgaris and M.convolutum respectively. For brackish waters, the use of natural bay water (6.7 PSU) in place of an artificial equivalent (distilled water + sea salt) resulted in 33.9% and 28.7% higher DW/L value. Selecting the optimal nitrogen source accelerated biomass production by another 10% for C.vulgaris, while no clear nitrogen source preference was observed for M.convolutum. Modifications to the photoperiod and wavelength of light proved efficient growth with continuous exposure to white light (24h) and highlighted achieved similar growth results with red or blue light in addition to white light in the 12h White/12h Colour photoperiod. In summary, with the use of multi-stage optimization, efficient and physiologically optimized growth was achieved, approximately 57% higher in final biomass DW/L regarding standard conditions, for both green algae strains. We discussed the the advantages of using natural waters and the opportunities presented by the physiological adaptation of brackish strains for growth on fresh and brackish water - the controlled application of salinity stress using local environmental waters in mass culture..

A Master-Slave Salp Swarm Algorithm Optimizer for Hybrid Energy Storage System Control Strategy in Electric Vehicles

Pure electric vehicles provide an enticing ecofriendly alternative to traditional fossil fuel combustion engine locomotives. Batteries have primarily been used to store energy in electric vehicles; however, peak load demand and transient power leading to decreased battery lifespan have bred interest in hybrid energy storage systems in electric vehicles. Management of energy drawn from a hybrid energy storage system (HESS) in electric vehicles is a real-time multistage optimization problem aimed at minimizing energy consumption while aptly distributing energy drawn from the battery and capacitor to enhance the battery life cycle. This paper explores the feasibility of a master-slave salp swarm optimization algorithm (MSSSA) (metaheuristic algorithm) in a HESS control strategy for electric vehicles. Introducing a master-slave learning approach to the salp swarm algorithm (SSA) improves its performance by increasing its convergence rate while maintaining a balance between exploration and exploitation phases of the algorithm. A comparison of the MSSSA results with the SSA (salp swarm algorithm), DA (dynamic algorithm), WOA (whale optimization algorithm), MFO (moth flame optimization algorithm), GA (genetic algorithm), and PSO (particle swarm optimization algorithm) on benchmark test functions and dynamic program simulation of an electric vehicle’s HESS control strategy and shows preeminence of the MSSSA control strategy for HESS.

A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction

Offshore wind power prediction is the basis for safe operation and grid dispatch. However, it is difficult due to the high volatility. Aiming at the three shortcomings of current methods: lack of analysis of the impact of multiple variables; the reconstruction method of decomposition components often adopts the summation method; the traditional machine learning prediction methods are not accurate enough, while the deep learning methods are prone to overfitting. This paper proposes a multi-variable hybrid prediction model based on multi-stage optimization and reconstruction prediction. Firstly, the isolated forest is used for data preprocessing. Secondly, the power sequence is decomposed by the variational modal decomposition optimized by the gray wolf algorithm to reduce the non-stationarity. Thirdly, the kernel extreme learning machine optimized by sparrow algorithm is used to predict. Finally, the reconstruction prediction is carried out through the long short-term memory network. Compared with the traditional machine learning method and the deep learning method, the model is effectively improved on two European offshore datasets. Then the interval prediction based on this model further verifies the accuracy and reliability.

Highly efficient passive Tesla valves for microfluidic applications

A multistage optimization method is developed yielding Tesla valves that are efficient even at low flow rates, characteristic, e.g., for almost all microfluidic systems, where passive valves have intrinsic advantages over active ones. We report on optimized structures that show a diodicity of up to 1.8 already at flow rates of 20 μl s−1 corresponding to a Reynolds number of 36. Centerpiece of the design is a topological optimization based on the finite element method. It is set-up to yield easy-to-fabricate valve structures with a small footprint that can be directly used in microfluidic systems. Our numerical two-dimensional optimization takes into account the finite height of the channel approximately by means of a so-called shallow-channel approximation. Based on the three-dimensionally extruded optimized designs, various test structures were fabricated using standard, widely available microsystem manufacturing techniques. The manufacturing process is described in detail since it can be used for the production of similar cost-effective microfluidic systems. For the experimentally fabricated chips, the efficiency of the different valve designs, i.e., the diodicity defined as the ratio of the measured pressure drops in backward and forward flow directions, respectively, is measured and compared to theoretical predictions obtained from full 3D calculations of the Tesla valves. Good agreement is found. In addition to the direct measurement of the diodicities, the flow profiles in the fabricated test structures are determined using a two-dimensional microscopic particle image velocimetry (μPIV) method. Again, a reasonable good agreement of the measured flow profiles with simulated predictions is observed.

Coordinated optimal design of school building envelope and energy system

In the design of school buildings, it is necessary to trade off the effects of energy, thermal comfort, daylighting, and life cycle cost. In this paper, a multi-objective coordinated design optimization method of school buildings is proposed to realize the efficient global design optimization of school building envelopes and energy systems. The method is mainly divided into three modules: coordinated design variables, envelope optimization and energy system optimization. In the process of coordinated design optimization, through the iterative method based on multi-objective optimization of envelope, multi-attribute decision-making for Pareto scheme set and single-objective optimization of energy system, the error between the optimal values of the coordinated design variable of the envelop and the energy system is reduced, and finally the global optimal design scheme is obtained. The effectiveness of the proposed optimization method is verified by a case study. The results show that the proposed coordinated design optimization method is stable and reliable. Under the policy of selling electricity, the life cycle cost of the building energy system is -$665,459.50 and the payback period is 6.12 years. Under the policy of not selling electricity, the life cycle cost of the building energy system is $75,292.50, which is 2.75% lower than the traditional multi-stage optimization.

Consensus Reaching in Multiple Attribute Group Decision Making: A Multi-Stage Optimization Feedback Mechanism With Individual Bounded Confidences

Existing consensus models focus on improving the group consensus level, but ignore whether a higher group consensus level means higher mutual acceptance of decision makers. In the field of opinion dynamics, the bounded confidence model asserts that the decision makers will accept the preferences of others within a neighborhood of theirs with width a certain confidence level. Inspired by this research methodology, this article develops a consensus model to address the acceptance issue based on individual bounded confidences. Specifically, a bounded confidence-based consensus measure is designed to measure the level of group mutual acceptance, and a multi-stage optimization feedback mechanism based on individual bounded confidences is proposed to maximize the group mutual acceptance and minimize the amount of preference adjustment. A numerical example and a simulation analysis are included to illustrate the use of the model and to justify its effectiveness, respectively.

Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm

Batch process is an important type of industrial production process, and the process mechanism is complex. It is difficult to accurately describe the dynamic changes of the production process of multi-stage time-varying batch process. In addition, the data of batch process contain not only global information but also local information. The traditional neighborhood preserving embedded algorithm is used to maintain the local geometric structure of data while ignoring the global information, and the extracted latent variables cannot fully characterize batch process. Therefore, we propose a multi-stage optimization regularized neighborhood preserving embedding (ORNPE) algorithm. First, the multiple process stages are separated by affinity propagation (AP) algorithm. Second, based on maintaining local information of neighborhood preserving embedding algorithm, slow feature analysis algorithm is used to extract dynamic time-varying global information. Then, cross-entropy is used to optimize the global information, and the extraction ability of the global information is improved. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The effectiveness and advantages of the proposed algorithm based on monitoring strategy are illustrated by the penicillin fermentation process and a semiconductor industry process.