Efficient Optimization Scheme Research Articles

Simulation and Optimization System of Automated E-Commerce Logistics Warehouse Allocation Network Based on Intelligent Algorithm

The prosperity of e-commerce has made our lives more convenient, but the logistics behind it has been silently paying. The e-commerce industry is expanding at an unprecedented rate, and with it comes explosive growth in logistics demand, especially in the warehouse distribution link. In order to meet this demand, the warehouse distribution system needs to be improved both in terms of technology and efficiency. This rapid development also brings a series of problems. First of all is the problem of logistics congestion. Due to the surge in orders, especially during peak shopping periods, logistics centers are often under great pressure. This not only affects the speed of delivery, but can also lead to damage or loss of goods. The purpose of this paper is to find an effective method to optimize the logistics warehouse allocation system of e-commerce, and to provide an efficient and reliable logistics optimization scheme for e-commerce enterprises. In this paper, data analysis and simulation methods are used to analyze. The experimental results show that PSO has the largest improvement in the order clerk operation efficiency (13%) and the smallest improvement in the order status (1%).

Energy Efficient Optimization Scheme for Wireless Powered Active RIS in Downlink System

Energy Efficient Optimization Scheme for Wireless Powered Active RIS in Downlink System

An efficient optimization strategy applied to spacecraft smart radiation devices design

An efficient optimization strategy applied to spacecraft smart radiation devices design

A Comprehensive Review of FACTS Controllers in Nigeria Power Systems Network for Enhanced Performance on Variable Loads

Interest on FACTS controllers in the scientific and engineering community have increased due to their high reliability, low cost, and absence of infrastructural overhauling. In the context of global sustainability, it is required that FACTS controllers must fulfill physical, technological, and environmental requirements, through design and optimization studies. This study provides a comprehensive evaluation of the state-of-the-art technologies relevant to multi-objective optimization and performance analysis of FACTS controllers. Types, general overview, FACTS deployed in the Nigerian power systems and multi-objective location optimization and their impact were discussed. Several efficient optimization strategies from recent research works are highlighted and compared, showing that there are research gaps on the integration of advanced control algorithms and use of robust optimization methods.

Accurate Monocular SLAM Initialization via Structural Line Tracking.

In this paper, we present a novel monocular simultaneous localization and mapping (SLAM) initialization algorithm that relies on structural features by tracking structural lines. This approach addresses the limitations of the traditional method, which can fail to account for a lack of features or their uneven distribution. Our proposed method utilizes a sliding window approach to guarantee the quality and stability of the initial pose estimation. We incorporate multiple geometric constraints, orthogonal dominant directions, and coplanar structural lines to construct an efficient pose optimization strategy. Experimental evaluations conducted on both the collected chessboard datasets and real scene datasets show that our approach provides superior results in terms of accuracy and real-time performance compared to the well-tuned baseline methods. Notably, our algorithm achieves these improvements while being computationally lightweight, without the need for matrix decomposition.

Exploring nutrient supplementation and bioprocess optimization to improve the production of lentiviral vectors in serum-free medium suspension cultures.

The use of lentiviral vectors (LV) in gene therapy has been growing in recent years. To meet the increasing clinical demand, LV production platforms will benefit from improved productivity and scalability to enable cost-effective manufacture of LV-based therapies. Here we report the adaptation of 293T cells to serum-free suspension cultures and the improvement of LV yields through transfection parameters optimization, process intensification and medium supplementation with nutrient boosters. Cells were sequentially adapted to different serum-free culture media, transfection parameters were optimized and the two best-performing conditions were selected to explore process intensification by increasing cell density at the time of transfection. LV production at higher cell densities increased volumetric titers up to 12-fold and lipid supplementation was the most efficient metabolic optimization strategy further enhancing LV productivity by 3-fold. Furthermore, cell concentration was identified and validated as an important source of transfection variability impairing cellular uptake of DNA polyplexes, impacting transfection efficiency and reducing LV titers down to 6-fold. This work contributes to improving LV-based gene therapy by establishing new scalable manufacturing platforms and providing key metabolic insights, unveiling important bioreaction parameters to improve vector yields.

Energy efficiency optimization strategies for greenhouse‐based crop cultivation: A review

AbstractWorldwide, food scarcity is becoming a debatable concern among the scientific fraternity due to the increased populace, leading to decreased arable land. This has compelled us to explore various innovative and technological solutions, for example, large‐scale greenhouse farming, to meet the surging demand for field production. In this context, research efforts have been continually made by various scientists and researchers to explore more control strategies/algorithms for keeping the indoor climate comfortable and enhancing the greenhouse's energy effectiveness. Considering this, an initiative was made to summarize the documented research findings in the last decade focusing on energy‐efficient greenhouse‐based crop cultivation. The findings of some studies considering selective parametric conditions have been presented in graphs/tables for reader clarity and discussion. Initially, the studies on existing energy efficient strategies, parameters, monitoring systems, sensing networks, and control algorithms have been discussed. A state of the art review found that control strategies are essential in low‐energy greenhouses since they influence crop yield and cost. It was observed that advanced control algorithms and energy conservation in greenhouses received more attention due to wide spread application, high compatibility, low‐cost, and user‐friendly operations. In terms of future perspectives, it is anticipated that the development of machine learning, big data, and artificial intelligence, combining these technologies with traditional and advanced control strategies would lead to a revolution in the management of greenhouse energy.

A high-resolution energy use efficiency assessment of China’s staple food crop production and associated improvement potential

Improving production efficiency of existing agricultural systems is key to promoting sustainable agricultural production. Energy use efficiency in crop production is a key and widely applied index used to measure agricultural production efficiency. Understanding the current status of energy use efficiency in crop production at high spatial resolutions is requisite for developing energy-saving and efficient crop production optimization strategies. This study provides a first attempt at systematically evaluating the county-level energy use efficiency status of China’s three staple food crops (i.e., wheat, maize, and rice) and their associated improvement potential based on the Energy Return on Investment (EROI) method. Results show that average EROI values of wheat, maize, and rice production (i.e., 0.94, 1.1, and 1.3, respectively) substantiate low energy use efficiency under a high improvement potential. Moreover, the energy use efficiency of staple food crop production exhibits considerable spatial heterogeneity. The top 25% counties with the highest EROI values are mainly concentrated in wheat-producing areas of the North China Plain, maize-producing areas of northeastern China and Inner Mongolia, and rice-producing areas of the central Yangtze River region. Crop yields and agricultural machinery inputs are the main EROI driving factors. Furthermore, scenario analysis shows that regional EROI gaps in wheat, maize, and rice production could be narrowed by increasing yields and improving agricultural machinery operation efficiency. Overall, this study provides an improved understanding of the current energy use efficiency status of staple food crop production in China. Recommended solutions are also proposed to improve the EROI of staple food crops. This provides scientific support in formulating regional optimal management strategies for crop production and guidance for farmers to conduct more efficient production practices. Additionally, our framework can also be adopted and built upon in regions throughout the world that experience similar low energy use efficiency conditions in agricultural production.

An iterative gradient descent-based reinforcement learning policy for active control of structural vibrations

A contemporary policy gradient-based optimization scheme is presented for active structural control by exerting the concept of reinforcement learning (RL). The RL-based control algorithm is demonstrated both in proportional (P) state, and proportional-integral (PI) state-output feedback approaches, wherein, the latter strategy is deliberated based on the theory of servo-mechanism. The search for optimal P and PI controller parameters in a training sequence is attained by engaging an efficient gradient descent-based optimization strategy. The utilization of gradient-based sequence within the RL framework accelerates the learning protocol, ensuring effective dissipation of structural energy, and achieving suboptimal control. The proposed algorithms are validated through numerical experiments on two different structural systems: (i) a quarter car model subjected to periodic road excitation, having a single actuator and presented in continuous time, and (ii) an 8-story building model subjected to random seismic excitation, having multiple actuators and presented in discrete time. Practical implementation concerns of control strategies are thoroughly investigated by considering perturbations in model parameters and input forces. The findings from this study indicate that the RL-based P and PI controllers exhibit high stabilizing performance, and are applicable in both analog and digital domains. Finally, it has affirmed that the proposed RL algorithms exhibit relatively low computational complexity in real-time, and thus, open up a wide range of perspectives for its application in large-scale complex structures.

An Efficient Modular Optimization Scheme for Unbalanced Active Distribution Networks With Uncertain EV and PV Penetrations

Integrated control strategies with Network Reconfiguration (NR), Demand Response (DR), and voltage control can reduce peak demand, energy loss, and system-wide unbalances in modern three-phase active Electric Distribution Networks (EDNs). However, simultaneous handling of these strategies is computationally complex and challenging. This paper presents a stochastic optimization formulation that slices the problem, solves the sub-problems separately, and splices them back to find optimal solutions efficiently. At the outset, all constraint-violating configurations are eliminated using a graph-theory-based edge traversal search developed from the classic Knuth’s Algorithm-S. Subsequently, deploying suitable indices, cyclic NR-DR assignments are performed to find near-optimal topologies and load schedules concerning the minimum loss, peak load, and unbalances. Further, Voltage Regulators (VRs) are set to achieve loss and unbalance reduction with minimal tap operations. The proposed scheme is tested on the modified IEEE 123-node test feeder with extensive EV and PV penetrations. The results show that this modular scheme provides superior solutions with an almost 75% reduction in time than the conventional co-optimization method. Various other case studies illustrate the effectiveness of the proposed scheme.

Efficiency Optimization and Control Strategy of Four-Switch Buck–Boost Converter for Wide Conversion Ratio

An improved control strategy for the four-switch buck-boost (FSBB) converter is proposed to improve the efficiency in a wide conversion ratio and wide loads. The conventional duty-cycle-controlled FSBB converter can not achieve the zero-voltage switching (ZVS) for all the switches, while the converter has a dead zone issue. The proposed control strategy is controlled by the duty cycle and phase shift control. The control law is derived from the analytical solution of the optimized current stress with the constraint of the ZVS for all the switches. The working modes in the proposed control strategy can cover the whole load in a wide conversion ratio. Furthermore, the dead zone issue is removed. The modulation surface and output power surface versus the conversion ratio and control variable demonstrate the working modes are switched seamlessly. The control strategy is implemented in the digital controller. Finally, the experimental results verify the higher efficiency and dynamic performance of the control strategy.

Resource Optimization Strategy in Phased Array Radar Network for Multiple Target Tracking When Against Active Oppressive Interference

The phased array radar network has the ability to track multiple targets simultaneously through the cooperative work of several active nodes, which brings significant advantages in tracking target under oppressive interference. In this article, an efficient resource optimization strategy (ROS) is developed in this scenario. The optimization model is formulated with the aim of countering active oppressive interference by dynamically assigning tracking tasks and allocating transmit power to each node. The architecture of information fusion considered in this article is centralized so that the detection performance and the parameter estimation accuracy can be improved. Since the predicted conditional Cramer–Rao lower bound (PC-CRLB) can provide an accurate performance lower bound for target tracking accuracy, the objective function is designed to improve the sum of weighted PC-CRLBs of multiple targets. By introducing the relaxation technique and the sorting algorithm, this mixed integer nonlinear programming problem is converted into a two-stage nonconvex problem, and the efficient solutions are obtained through the modified Zoutendijk method of feasible directions algorithm. Numerical simulations demonstrate the effectiveness of the proposed ROS.

Cocatalysts-Photoanode Interface in Photoelectrochemical Water Splitting: Understanding and Insights.

Sluggish oxygen evolution reactions on photoanode surfaces severely limit the application of photoelectrochemical (PEC) water splitting. The loading of cocatalysts on photoanodes has been recognized as the simplest and most efficient optimization scheme, which can reduce the surface barrier, provide more active sites, and accelerate the surface catalytic reaction kinetics. Nevertheless, the introduction of cocatalysts inevitably generates interfaces between photoanodes and oxygen evolution cocatalysts (Ph/OEC), which causes severe interfacial recombination and hinders the carrier transfer. Recently, many researchers have focused on cocatalyst engineering, while few have investigated the effect of the Ph/OEC interface. Hence, to maximize the advantages of cocatalysts, interfacial problems for designing efficient cocatalysts are systematically introduced. In this review, the interrelationship between the Ph/OEC and PEC performance is classified and some methods for characterizing Ph/OEC interfaces are investigated. Additionally, common interfacial optimization strategies are summarized. This review details cocatalyst-design-based interfacial problems, provides ideas for designing efficient cocatalysts, and offers references for solving interfacial problems.

An efficient optimization strategy for vapor compression–absorption-based cascaded refrigeration system using various evolutionary techniques

ABSTRACT A novel optimization strategy for a compression-absorption cascaded refrigeration system (CACRS) consisting of a single effect vapor absorption system (VARS) and vapor compression system (VCRS), which employs several absorbent solution-refrigerant-refrigerant pairs to consider the combination of absorbent solution-refrigerant in VARS and refrigerants in VCRS as an optimization variable, is successfully implemented using various evolutionary optimization techniques. This article analyzes three different absorbent solution-refrigerant in the VARS with eleven different refrigerants in the VCRS. A nonlinear objective function is formulated based on exergy and environmental analysis, which is minimized using 10 different evolutionary optimization techniques. The proposed novel strategy reduces the complexity of using optimization techniques to select the best optimum absorbent-refrigerant-refrigerant pair. Differential evolution with multi-population-based ensemble of mutation strategies determined the minimum total annual cost as 13,137.78 US$/year for (CaCl2-LiBr-LiNO3-)-H2O-R152a. Lower condenser temperature and higher temperature difference in the cascade-condenser resulted in a minimum value for the total annual cost.

An Efficient Dynamic Optimization Algorithm for Path-Constrained Switched Systems.

Dynamic optimization is one of the model-based adaptive reinforcement learning methods, which has been widely used in industrial systems with switching mechanisms. This article presents an efficient dynamic optimization strategy to locate an optimal input and switch times for switched systems with guaranteed satisfaction for path constraints during the whole time period. In this article, we propose a single-level algorithm where, at each iteration, gradients of the objective function with respect to switch times and the system input are evaluated by solving adjoint systems and sensitivity equations, respectively. Then the optimization of the input is performed at the same iteration with that of the switch time vector, which greatly reduces the number of nonlinear programs (NLPs) and computational burden compared with multistage algorithms. The feasibility of the optimal solution is guaranteed by adapting a new policy iteration method proposed to switched systems. It is proven that the proposed algorithm terminates finitely, and converges to a solution which satisfies the Karush-Kuhn-Tucker (KKT) conditions to specified tolerances. Numerical case studies are provided to illustrate that the proposed algorithm has less expensive computational time than the bi-level algorithm.

Design and Optimization of Linking Matrix for a JSCC System Based on DP-LDPC Codes.

Although extensive optimization of encoding and decoding schemes for joint source-channel coding (JSCC) systems has been conducted, efficient optimization schemes are still required for designing and optimizing the linking matrix between variable nodes of the source code and check nodes of the channel code. A scheme has been proposed for design and optimization of linking matrix with multi-edges by analyzing the performance of the JSCC system using the joint protograph extrinsic information transfer algorithm to calculate decoding thresholds. The proposed scheme incorporates structural constraints and is effective in designing and optimizing the multi-edges in linking matrix for the JSCC system. Experimental results have demonstrated that the designed and optimized linking matrix significantly improves the performance of the JSCC system. Furthermore, the proposed scheme reduces the complexity of the solution space for the optimized example.

Simple multiple kernel k-means with kernel weight regularization

Multiple kernel clustering (MKC) aims to determine the optimal kernel from several pre-computed basic kernels. Most of MKC algorithms follow a common assumption that the optimal kernel is linearly combined by basic kernels. Based on a min–max framework, a newly proposed MKC method termed simple multiple kernel k-means can acquire a high-quality unified kernel. Although it has achieved promising clustering performance, we have observed that it cannot benefit from any regularization or prior knowledge, thus the learned kernel weight coefficients may be seriously sparse or over-selected. To tackle this issue, we have developed a new algorithm termed simple multiple kernel k-means with kernel weight regularization (SMKKM-KWR), where we introduce average coefficients to avoid too sparse kernel weights. Specially, we add the average kernel coefficients as a regularization term to prevent the learned weight coefficients being far from the average values. After that, an efficient optimization strategy is proposed to solve the new resultant problem. By this way, the unified partition can learn clustering structure from fusion information of all the kernel views, with the goal to reach better clustering performance. Extensive experiments on nine benchmark datasets and four large-scale datasets have demonstrated the effectiveness and efficiency of the proposed algorithm.

Uniform design and deep learning based liquid lens optimization strategy toward improving dynamic optical performance and lowering driving force.

An efficient optimization strategy for liquid lens combining the uniform design and the deep learning is proposed to achieve improved dynamic optical performance and lowering driving force simultaneously. The membrane of the liquid lens is designed into a plano-convex cross-section, in which the contour function of the convex surface as well as the central membrane thickness is especially optimized. The uniform design method is initially utilized to select a part of uniformly distributed and representative parameter combinations from all possible parameter range, and their performance data is then obtained through simulation using MATLAB to control COMSOL and ZEMAX. After that, a deep learning framework is employed to build a four-layer neural network with its input and output layer representing the parameter combinations and the performance data, respectively. After 5 × 103 epochs, the deep neural network has undergone sufficient training, demonstrating effective performance prediction capability for all parameter combinations. Finally, a "globally" optimized design can be obtained by setting appropriate evaluation criteria which take the spherical aberration, the coma and the driving force into consideration. Compared with the conventional design using uniform membrane thickness of 100 µm and 150 µm as well as the previously reported "locally" optimized design, distinct improvements in the spherical and the coma aberrations across the entire focal length tuning range have been achieved, whilst the required driving force is largely reduced. In addition, the "globally" optimized design exhibits the best modulation transfer function (MTF) curves and provides the best image quality.

Efficient discrete cross-modal hashing with semantic correlations and similarity preserving

With its merits in query speed and memory footprint, hashing has elicited considerable monument in cross-media similarity retrieval applications. Many label-dependent supervised hashing methods have been proposed for similarity searching across modality boundaries. However, the current cross-modal hashing (CMH) works are subjected to severe information loss, and their performances may dramatically degrade because of the expensive costs of constructing affinity graphs, inadequate mining of label information, and disregarding label correlations. To facilitate these problems, we propose an Efficient Discrete Cross-Modal Hashing (EDCH) in which an asymmetric model is introduced, which not only conveys external semantic information via embedding high-order labels but also preserves internal modality attributes by introducing binary representations and common subspace. To fully use label semantic information, we integrate the semantic supervised intersection scheme and the category correlations embedding in a shallow framework. Moreover, we elaborately develop an efficient and effective discrete optimization strategy to learn binary representations and a novel mutual linear projection to strengthen the capability and effectiveness of hash functions. Comprehensive experiments are conducted on three representative datasets to evaluate our method. The results validated that our method achieved promising and competitive retrieval performance and surpasses several typical and cutting-edge approaches.

Power Quality Improvement of Hybrid Distributed System Using MGWO Technique-Based DVR and DSTATCOM

This study proposes a methodology for enhancing the control of power in renewable energy with microgrids using an efficient optimization strategy. The methodology involves the use of MGWO for maximum electricity factor tracking tuning and input parameter PV optimization. A two-level PWM generator block is used to generate PWM pulse converters. The Enhanced Whale Optimization (EWO) approach is implemented to adjust the PID controller parameters, and pulse is fed to the DC to AC inverter. To address electricity problems together with harmonic distortion, voltage sag and swell, and fluctuation, FACT devices (DSTATCOM and DVR) are employed. The performance estimation of DSTATCOM & DVR devices is evaluated in terms of voltage grid, current grid, current load, voltage shunt, voltage swell/sag, THD, and ITAE. The results show the implementation of the suggested FACT device effectively eliminates power quality issues. A comparative analysis between DSTATCOM & DVR shows that DSTATCOM offers a lower distortion rate for grid transmission compared to DVR.