Dual Optimization Research Articles

A data-driven optimisation method for a class of problems with redundant variables and indefinite objective functions

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable.

Synthesis of Composite Titanate Photocatalyst via Molten Salt Processing and Its Enhanced Photocatalytic Properties.

Photocatalysis plays a pivotal role in environmental remediation and energy production and improving the efficiency of photocatalysts, yet enhancing its efficiency remains a challenge. Titanate has been claimed to be a very promising material amongst various photocatalysts in recent years. In this work, a novel composite photocatalyst of sodium titanate and potassium titanate was synthesized via a simple hydrothermal and molten salt calcination method. Low melting point nitrate was added in the calcination process, which helps reduce the calcination temperature. The as-prepared composite sample showed excellent photocatalytic performance compared with commercial P25 in the visible light range. According to the characterization of XRD, SEM, TEM, BET, UV-Vis, and photocatalytic property testing, the composite's photocatalytic performance results are due to the dual optimization brought about by the layered structure and composite of titanium salts forming a heterojunction. We believe that the composite has significant application potential for the use of titanate in the field of photocatalysis. Notably, this study employed well-documented synthesis methods and adhered to established protocols for experimental procedures.

Optimization strategy for SAM in nuclear power plants based on NSGA-II

Abstract The Severe Accident Management Guide (SAMG) is an important component of nuclear safety regulations. Many studies are being conducted to optimize severe accident management (SAM) strategies. To ensure the safety of nuclear power plants, decision makers need to monitor multiple parameters with security threats. Therefore, it is particularly important to search optimal SAM strategies under different numbers of mitigation targets. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is an evolutionary algorithm that does not require derivative differentiation and is capable of population search. In this study, a nuclear power plant accident optimization strategy is developed using the Modular Accident Analysis Program (MAAP) in conjunction with NSGA-II. The strategy enables decision makers to consider multiple mitigation objectives in a complex decision environment. Focusing on the CPR1000, this study applies the optimization strategy to automatically search for optimal mitigation strategies for small break loss of coolant accident (SBLOCA) and station blackout hot leg creep rupture accidents (SBOHLCR). Comparing the optimization results with the basic accident sequence, it is found that the reactor pressure vessel (RPV) failure time is delayed from 72,702 s to 128,730 s under SBLOCA and from 23,828 s to 28,363 s under SBOHLCR. This study has also verified that the optimal SAM strategy obtained by the strategy through dual objective optimization has better mitigation effects than a strategy that only considers one objective. This optimization strategy has the potential to be applied to other types of severe accident management studies in the future.

Power Optimization in a Multicell D2D Communication for Smart City in an mm-Wave Cellular Network: An mIoT Perspective

Device-to-device (D2D) communication ushers the realization of massive Internet of Things (mIoT) network for smart cities through long term evolution-advanced (LTE-A). In a multi-cell mm-Wave environment, acute interference from adjacent cells degrades the signal quality. Thus, our work focuses on the minimization of interference from the neighboring cells through optimal resource allocation and power optimization. Rate splitting multiple access (RSMA) technique is employed where the message to be transmitted is divided into two parts. Two modes for resource allocation are formulated which aims to maximize the throughput under certain interference constraints. Radius of coverage expression is derived for switching of modes to take place. Successive interference cancellation (SIC) is introduced as a power constraint which enhances the overall performance of the network. Subsequently, Lagrange’s dual optimization method is utilized for optimizing the D2D transmit power and also lowering the computational complexity of the overall network. Simulation results depict the overall performance efficiency in terms of transmission rate and signal strength. The results show that with the increase in difference of transmit power allocated to the common and private message, overall transmission rate also increases significantly. Finally, comparison with existing scheme validates the efficiency of the proposed work.

One-step processing of waste dredged slurry into planting soil by targeted pretreatment and vacuum filtration

A novel method, targeted pretreatment and vacuum filtration (TP-VF), is introduced and validated in this study. TP-VF offers a one-step solution for efficiently dewatering dredged slurry (DS) characterized by heavy metal contamination, high salinity, and excessive moisture content. This innovative approach combines targeted pretreatment (TP) and vacuum filtration (VF) to transform DS into the viable planting soil. TP encompasses a dual optimization strategy that enhances both slurry dewatering efficiency and the quality of the resultant planting soil (cake). By employing flocculation pretreatment, TP increases the size of flocs and enlarges cake pores, leading to improvements in dewatering efficiency and infiltration rates. Additionally, targeted pretreatment results in the discharge of approximately 90% of heavy metal ions and most salts with the filtrate. Remarkably, the addition of chelating agents and freshwater as part of the pretreatment process positively impacts soil quality without compromising floc size or dewatering efficiency. Comparatively, TP-VF demonstrates a substantial reduction of 35.81% in operational costs when contrasted with the traditional two-step process, concurrently diminishing the potential for secondary environmental pollution. This study highlights TP-VF as a promising advancement in sustainable slurry management, addressing pressing environmental challenges while optimizing resource utilization.

GIS-based MCDM dual optimization approach for territorial-scale offshore wind power plants

Despite the relevant potential of offshore resource to substantially mitigate the effects of climate change through the generation of renewable electricity, the full exploitation of such wind resource currently remains incomplete. To promote offshore wind energy projects, it is crucial to engage in comprehensive planning processes that encompass technical, social, environmental, stakeholder, and political considerations. Within this framework, this paper presents a combined approach based on Geographical Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methodology to optimize the selection of offshore wind farm locations. The proposed methodology includes annual net electricity production as a critical factor in the decision-making process, which has been previously optimized with regard to the wake effect. Subsequently, the determination of potential locations involves key criteria such as technical feasibility, environmental impact, economic viability, and power generation potential. This MCDM-GIS approach was applied and evaluated in a case study along the Spanish coastline, considering 92 initial alternatives. The results indicate that the offshore wind energy targets established for Spain in 2030 and 2050 should be reevaluated, as the optimal utilization of the available area represents only 16% of the total potential area. Based on the aforementioned criteria, a suitability map was generated by integrating all relevant map layers, including their respective buffer zones.

An Efficient Authentication Approach with Optimization Algorithms and Elliptical Curve Cryptography for Cloud Environment

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. Security is very important for any kind of networks. As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. Attacks are one of the biggest concerns for security professionals. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies. This paper presents a dual optimizer based key generation method for the improving the authentication with Elliptical Curve Cryptography (ECC) encryption algorithm. The optimal private and secret key for the encryption and decryption are obtained with the optimization techniques like Animal Migration Optimization (AMO), and Brain Storm Optimization (BSO) for strengthening the security in the Cloud Computing environment.

A novel binocular vision-robot hand-eye calibration method using dual nonlinear optimization and sample screening

To reduce the effects of random errors in vision robotic systems, a double-layer LM (Levenberg- Marquardt) optimization (DLMO) method was designed to improve the calibration accuracy of the robot hand-eye matrix based on the principle of nonlinear optimization. Firstly, a hand-eye matrix equation was established, and the initial value of the hand-eye matrix was solved by using a linear least square method. Secondly, Euler angle transformation was performed on the rotation matrix part in the hand-eye matrix, to ensure the orthogonality of the rotation matrix. Then, an optimization model of the hand-eye matrix was constructed, and the initial value of the hand-eye matrix was optimized for the first time by using the traditional LM nonlinear optimization method. Finally, the optimization model of the hand-eye matrix was modified, and then the LM nonlinear optimization method with the function of sample point screening was used to optimize the hand-eye matrix for the second time. Using the proposed DLMO, the hand-eye calibration tests were carried out on two industrial robots equipped with binocular vision systems. The average position errors of the calibration results were 0.33 mm and 0.51 mm, respectively. The results showed that the accuracy of the proposed calibration method met the working requirements of the vision-robot in the industrial field.

Comprehensive assessment of building energy consumption in hot summer and cold winter areas based on carbon tax considerations

With the rapid development of economy and urbanization in China, cities are expanding rapidly and more constructions are being built, then lead to the fast-growing part in urban residential energy consumption. In China’s hot summer and cold winter regions, many buildings are not centrally heated and commonly rely on electrical equipment such as air conditioners which are all energy-inefficient thermoregulation devices. In order to analyze the relationship between building energy consumption and the energy efficiency ratio (EER) of air conditioning and the area of photovoltaic (PV) on the roof, the influence of the building envelope on building energy consumption in hot summer and cold winter regions in China is clarified. This paper uses energy plus software to analyze the impact of different EER and PV area on building energy consumption using a typical case study of a public health upgrading project in the eastern part of the Wuxing district, Huzhou, Zhejiang province in China. The simulation results show that the factor that has a greater impact on the energy consumption of the health center in summer is the PV area parameter of the building compared to the performance parameters of the air conditioning equipment. The PV area parameter settings and air conditioning equipment performance adjustments are dependent on the actual situation and the comprehensive energy saving rate of the outpatient building for public health in the eastern part of Tai Wu Xing District can exceed 50%. Finally, an economic analysis of the carbon tax and input costs is carried out, and the best combination is of which 60% of the area covered by Longi Green PV panels and an air conditioning efficiency ratio of 4.87. The research result combines renewable energy and efficient equipment to achieve dual optimization of environmental and economic aspects of building energy consumption, while providing reference opinions on the comprehensive evaluation method of building energy consumption from the perspective of carbon tax.

POBO: Safe and optimal resource management for cloud microservices

Resource management in microservices is challenging due to the uncertain latency–resource relationship, dynamic environment, and strict Service-Level Agreement (SLA) guarantees. This paper presents a Pessimistic and Optimistic Bayesian Optimization framework, named POBO, for safe and optimal resource configuration for microservice applications. POBO leverages Bayesian learning to estimate the uncertain latency–resource functions and combines primal–dual and penalty-based optimization to maximize resource efficiency while guaranteeing strict SLAs. We prove that POBO can achieve sublinear regret and SLA violation against the optimal resource configuration in hindsight. We have implemented a prototype of POBO and conducted extensive experiments on a real-world microservice application. Our results show that POBO can find the safe and optimal configuration efficiently, outperforming Kubernetes’ built-in auto-scaling module and the state-of-the-art algorithms.

Dual Optimization of Interface Defects and Hole Transporting Layers with Tetrathiafulvalene Derivatives for Antimony Selenosulfide Solar Cells

Modification of hole transporting layers (HTLs) to reduce the interface defects is an important strategy to promote hole extraction and suppress charge recombination for Sb2(S,Se)3 (antimony selenosulfide) solar cells. Herein, a simple doping method with the tetrathiafulvalene derivatives (MeS‐TTF and Cyano‐TTF) is developed to reduce the dangling bonding defects on the surface of Sb2(S,Se)3 films and improve the photovoltaic performances of Sb2(S,Se)3 solar cells. The studies show that the tetrathiafulvalene derivatives can effectively passivate the ionic defects through the chelation mechanism with Sb atoms and suppress the nonradiative recombination. Meanwhile, the MeS‐TTF dopant can also enhance the hole mobility of HTLs and improve the hole transport. The results indicate that the MeS‐TTF can achieve the dual function of interface defect passivation and hole mobility improvement. As a result of the dual‐function dopant, a significant increase in efficiency from 7.73% to 8.45% can be obtained. The outcomes of this study highlight that this dual‐function doping strategy is an effective method to passivate the interface defects and enhance the hole mobility for the excellent photovoltaic performances of Sb2(S,Se)3 solar cells.

Nanofiltration membrane based on a dual-reinforcement strategy of support and selective layers for efficient Mg2+/Li+ separation

With the consumption of lithium resources increasing year by year, the investigation of high-performance salt lake lithium extraction technology is extremely important. In order to effectively separate Mg2+ and Li+ from brine, a novel charge-positive nanofiltration (NF) membrane with high permeability and excellent Mg2+/Li+ separation factor was designed by interfacial polymerization (IP) in this work. A B15C5-MX-NF membrane was fabricated by blending hydrophilic two-dimensional (2D) MXene nanosheets into a PES membrane substrate to enhance the permeability, followed by the modification of polyamide (PA) layer with a crown ether molecule (Benzo-15-crown-5) to optimize the surface properties of membrane. The PA layer thickness was effectively reduced (58 nm) due to the enhancement in the interaction between the substrate membrane and PEI by the incorporation of MXene, while the addition of B15C5 provided additional Li+ permeation channels. The B15C5-MX-NF membrane exhibited excellent permeability (16.1 L·m−2·h−1·bar−1) and superior selectivity for Mg2+/Li+ (SLi, Mg of 15.8) under the dual optimization of membrane substrate and PA layer. In addition, it was demonstrated by molecular dynamics (MD) simulations that the B15C5 molecules in the B15C5-MX-NF membranes had a specific recognition ability of Li+ to allow its preferential permeation, thereby enhancing the Mg2+/Li+ selectivity of the membranes. This study provides a new idea for the preparation of NF membranes for efficient Mg2+/Li+ separation.

Multifunctional molybdenum-tuning porous nickel-cobalt bimetallic phosphide nanoarrays for efficient water splitting and energy-saving hydrogen production

The sluggish kinetics of the hydrogen evolution reaction (HER) and substantial barriers in the oxygen evolution reaction (OER) significantly impede its application in hydrogen production. To address this issue and enhance energy efficiency in hydrogen generation, we explored a high-activity alkaline HER catalyst while concurrently coupling it with the urea oxidation reaction (UOR). In this work, we designed and synthesized porous molybdenum (Mo)-modulated nickel-cobalt bimetallic phosphide nanoarrays (M0.3NCP@NF). This multifunctional self-supported electrocatalyst demonstrates superior performance in HER, OER, and UOR. The introduction of Mo, in the form of CoMoO4 nanoparticles, promotes interfacial electron transfer to reduce the electron density around the cations in phosphides, enhancing the kinetics and intrinsic activity. Furthermore, the morphological changes induced by Mo accelerate both electron and mass transfer processes. Density functional theory and operando electrochemical impedance spectroscopy indicate that Mo introduction optimizes the interaction with HER intermediate H*, facilitating the conversion to a high-valent active intermediate for OER and accelerating UOR kinetics. Benefiting from dual optimization of morphology and structure, the as-prepared M0.3NCP@NF electrocatalyst demonstrates outstanding HER, OER, and UOR performances. Notably, a full urea electrolysis device powered by M0.3NCP@NF operates with a cell voltage of only 1.53 V to achieve a current density of 100 mA cm−2. which is 240 mV lower than that of conventional water electrolysis, demonstrating the competitive potential of our approach for efficient and energy-saving hydrogen production, along with simultaneous urea wastewater remediation.

Conjugate Duality in Set Optimization via Nonlinear Scalarization

Conjugate Duality in Set Optimization via Nonlinear Scalarization

UAV-assisted ISAC network physical layer security based on Stackelberg game

This paper deals with the physical layer security for multi-unmanned aerial vehicle (UAV) assisted transmission of integrated sensing and communication (ISAC) signals from a dual-function base station (BS) to the ground user. Here, an active eavesdropper (Eve) on the ground eavesdrops on the information transmitted by the UAV network to the user. We propose a Stackelberg game model for the game problem of information security transmission between UAV and Eve, with perfect or imperfect channel state information (CSI) and cost information. To deal with the Stackelberg game problem, the dual optimization theory and KKT conditions are used to solve it, then we prove the existence and uniqueness of Stackelberg equilibrium (SE). At the same time, considering that the influence of ISAC signal’s sensing of Eve’s location on the distance between Eve and the user, and thus on the utility function of both sides of the game, this paper uses the Cramer–Rao bound (CRB) to represent the accurate performance of location sensing. Then the relaxation method is used for the non-convex optimization problem, and convex optimization tools can be used subsequently. Finally we notice that both operations have their own optimal resolution strategy, it is difficult to reach agreement, therefore an overall algorithm is proposed to solve the whole problem iteratively. In the simulation results, the effect of power on game and location sensing, and the effect of the distance between Eve and the user on the utility function of both sides of the game are given. In addition, we give the relationship between the CRB threshold and the transmit power, and verify the convergence of the game and overall algorithm.

Online trajectory optimization for safe autonomous overtaking with active obstacle avoidance

Autonomous driving with active obstacle avoidance in dynamic urban environment has attracted significant attention to improve road safety and traffic efficiency. In this paper, an online optimization-based trajectory planning method for autonomous overtaking is developed to prevent collision and realize safe efficient driving. Unlike traditional methods which solve the overtaking problem with segmented reference path in multi-stages with integer variables, this paper proposes a novel dual-variable trajectory planning framework to get the optimal trajectory for active collision avoidance. First, the nonlinear non-differentiable collision-free constraint between vehicles is reformulated using dual problem optimization. Then, the optimal trajectory for longitudinal and lateral movement is obtained jointly in a receding horizon optimization framework based on the optimized dual variable considering the safety and dynamic performance. The key novelty lies in the reformulation of the nonlinear optimal trajectory planning problem and the formulated whole optimization framework can be solved using efficient open-source solvers for online computation. Simulation studies in different dynamic cases are provided to show the efficiency and robustness for safe driving. The method reaches safe overtaking performance with much less calculation time. Real-world experiment with a static obstacle demonstrates its capability for the safe efficient trajectory planning and online implementation in autonomous driving.

Refraction correction for deep-water three-dimensional visual measurement based on multi-objective optimization.

Refraction-induced errors affect the accuracy of three-dimensional visual measurements in deepwater environments. In this study, a binocular camera refractive imaging model was established, and a calibration method for the refraction parameters was proposed for high-accuracy shape and deformation measurements in deep-water environments. First, an initial estimate of the refractive axis was obtained using a three-dimensional calibration target. Then, the errors in the distance between the spatial point pairs and the reprojection errors are taken as the dual optimization objectives, and the Non-dominated Sorting Genetic Algorithm II is applied to optimize the refraction parameters. To efficiently calculate the reprojection error, an improved numerical computation method is proposed to accelerate the calculation of the analytical forward projection. Underwater experiments were conducted to verify the method's effectiveness. The results showed that the average error of the absolute position of the reconstructed points was less than 1.1 mm and the average error of the displacement was less than 0.04 mm. This study provides a sound solution for accurate three-dimensional visual measurement in deep-water environments.

Passenger Flow Forecast for Low Carbon Urban Transport Based on Bi-Level Programming Model

<p>In the context of low-carbon city development, this paper further implements a rail transit passenger flow forecasting method to optimize energy consumption by combining the MMA allocation model with a two-tier planning model for carbon emission control. Through this approach, this paper not only fills the gap of rail transportation planning theories and methods compatible with low-carbon city development, but also emphasizes the importance of energy consumption in transportation planning. Based on a two-tier planning model, this paper considers the Starkberg game between multi-modal and multi-type passenger flow forecasting of rail transit and CO2 emissions of integrated transportation systems. By optimizing the allocation of users in the transportation network from the perspective of both users and planners, while optimizing the CO2 emissions of the integrated transportation system, the dual optimization of energy consumption and environmental benefits is achieved. The method will also be tested in Shanghai, and this paper will comparatively study three different carbon emission control schemes. By assigning passenger flows to the entire transportation system network in Shanghai based on information from the Fourth Integrated Transport Survey, including passenger flows on each road in the road network, passenger flows on each rail line, and characteristic indicators, this paper provides a reliable data base. This study provides a solid foundation for planning the layout of rail transit in a low-carbon mode and makes a positive contribution to sustainable urban development by optimizing energy consumption.</p> <p> </p>

An isogeometric collocation method for the static limit analysis of masonry domes under their self-weight

A novel isogeometric collocation method is proposed for the static limit analysis of axially-symmetric masonry domes subject to their self-weight. A shell-based static formulation is employed, alongside Heyman’s assumptions on masonry, to characterize the equilibrated and statically admissible stress states in the dome. As a distinctive feature of the approach, a vector stress function is introduced, generating point-wise self-equilibrated shell stress resultants in the dome. Accordingly, the classical minimum-thrust problem is formulated in terms of the unknown vector stress function, and the static admissibility conditions are enforced as the only optimization constraint. NURBS-based isogeometric analysis is adopted to accomplish the need for an accurate geometric description of the dome and a high-order continuous interpolation of the vector stress function. A discrete minimum-thrust problem is derived as a linear programming problem, with the static admissibility conditions checked at suitable collocation points. Instrumental to its solution is the computation of a particular solution of the equilibrium equations, which is obtained by an isogeometric collocation method. By a mechanical interpretation of the dual optimization problem, the settlement mechanism of the dome corresponding to its minimum-thrust state is also computed. Numerical results, dealing with a thorough convergence analysis, parametric analyses on spherical and ogival domes with parameterized geometry, and the real case of the Taj Mahal central dome are presented to prove the computational merit of the proposed approach.

Dual ant colony optimization for electric vehicle charging infrastructure planning

Dual ant colony optimization for electric vehicle charging infrastructure planning