Alternative Optimization Research Articles

Non-orthogonal unicast and multicast transmission with IRS-assisted Rate-Splitting Multiple Access in multiuser MISO systems

Abstract. In MISO systems, novel communication technologies such as NOMA and multicast transmission have attracted widespread attention to meet the diverse needs of users. However, traditional NOMA and multicast transmission schemes have certain limitations in dynamic interference management and resource allocation. To resolve this issue, this paper proposes a RSMA scheme assisted by phase quantized Intelligent Reflecting Surface (IRS) for downlink MISO systems with nonorthogonal transmission of unicast and multicast signals. In this scheme, through reconfiguring unicast and multicast signals and combining them into a super-common stream and partial unicast messages, RSMA achieves dynamic interference management of multi-user while effectively separating unicast and multicast streams. To maximize the weighted sum rate(WSR), we put forward an alternating optimization(AO) algorithm that utilizes Weighted Minimum Mean Square Error (WMMSE) estimation and convex set intersection representation, which jointly optimizes the equivalent beamforming matrix,rate allocation and phase shift matrix. Simulation results illustrate that the proposed single-layer RSMA exhibits performance closer to that of DPC encoding compared to traditional NOMA and MULP schemes. Furthermore, we extensively investigate the relationship between the WSR of RSMA and system parameters, providing important guidance for practical engineering applications.

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.

ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization

Diabetic retinopathy (DR) is a significant cause of vision impairment globally, emphasizing the importance of timely and precise detection to prevent severe consequences. This study presents an optimized Vision Transformer (ViT) model that incorporates Harris Hawk Optimization (HHO) to improve the automated detection of diabetic retinopathy (DR). The ViT architecture utilizes self-attention mechanisms to capture local and global features in retinal images. Additionally, HHO optimizes key hyperparameters to maximize the performance of the model. The proposed ViT-HHO model achieved exceptional performance on the APTOS-2019 and IDRiD datasets. Specifically, it achieved 99.83 % accuracy, 99.78 % sensitivity, 99.85 % specificity, and 99.80 % AUC-ROC on the APTOS-2019 dataset, surpassing traditional CNNs and alternative optimization techniques. The model exhibited strong generalization on the IDRiID dataset, achieving an accuracy of 99.11 % and an AUC-ROC of 99.12 %. The ViT-HHO model demonstrates the potential for enhancing the clinical detection of diabetic retinopathy (DR), providing high precision and reliability.•An optimized Vision Transformer (ViT) model was developed using HHO for improved detection of Diabetic Retinopathy (DR).•The model was validated on the APTOS-2019 and IDRiID datasets, demonstrating superior accuracy and AUC-ROC metrics.•The model's generalization and robustness were demonstrated through comprehensive performance evaluations.

Comparative Evaluation of Evapotranspiration and Optimization Schemes for Green Roof Runoff Simulations Using HYDRUS-1D

The use of green roofs, a low-impact development practice, can be an effective means of reducing direct runoff in urban centers. Green roof modeling can enable efficient design by preliminarily grasping the behavior of the green roof system according to specific configurations. In this study, we aimed to find appropriate evapotranspiration and parameter optimization schemes for HYDRUS-1D, a commonly used modeling tool for green roofs. Comparative studies of this sort in the context of green roof runoff modeling have not been conducted previously and are important in guiding users to overcome the difficulties of choosing the right numerical schemes for an accurate prediction of runoff from a green roof. As a study site, the Portland Building Ecoroof in Portland, Oregon, USA, was chosen, as green roof configurations and observed data for climate and runoff were available. From the simulation results of the runoff volume, the Blaney–Criddle method, which was considered an alternative, was found to be appropriate for calculating evapotranspiration from a green roof (R2 = 0.82) relative to the Hargreaves method built in HYDRUS-1D (R2 = 0.46). In addition, this study showed that the optimization method using the harmony search algorithm, which was proposed as an alternative optimizer, was better (R2 = 0.95) than that of the HYDRUS-1D’s own optimization module (R2 = 0.82) in calibrating HYDRUS-1D for green roof runoff. The findings are thought to be useful in guiding modelers who are considering using HYDRUS-1D for green roof runoff simulations.

Multiobjective optimization by using cutting angle methods and hypervolume criterion

We translate a multiobjective optimization problem into a single objective Lipschitz problem by using the hypervolume criterion of the Pareto set. Deterministic global optimization methods allow one to track the whole Pareto front rather than converging to a single non-dominated solution. We augmented an efficient hypervolume computation technique with hypervolume increment strategy, and established Lipschitzianity of the resulting objective function. We used two deterministic Lipschitz optimization methods together with the hypervolume objective and benchmarked them against some state-of-the-art alternative multiobjective optimization methods, establishing the competitiveness of the proposed approach.

Joint Power Allocation and Hybrid Beamforming for Cell-Free mmWave Multiple-Input Multiple-Output with Statistical Channel State Information.

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization-minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.

Skin lesion segmentation using deep learning algorithm with ant colony optimization

BackgroundSegmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis.ObjectiveThis paper seeks to evaluate the effectiveness of the Hybrid ResUNet model for skin lesion classification and assess its impact on optimizing ACO performance to bridge the gap between computational efficiency and clinical utility.MethodsThe study used a deep learning design on a complex dataset that included a variety of skin lesions. The method includes training a Hybrid ResUNet model with standard parameters and fine-tuning using ACO for hyperparameter optimization. Performance was evaluated using traditional metrics such as accuracy, dice coefficient, and Jaccard index compared with existing models such as residual network (ResNet) and U-Net.ResultsThe proposed hybrid ResUNet model exhibited excellent classification accuracy, reflected in the noticeable improvement in all evaluated metrics. His ability to describe complex lesions was particularly outstanding, improving diagnostic accuracy. Our experimental results demonstrate that the proposed Hybrid ResUNet model outperforms existing state-of-the-art methods, achieving an accuracy of 95.8%, a Dice coefficient of 93.1%, and a Jaccard index of 87.5.ConclusionThe addition of ResUNet to ACO in the proposed Hybrid ResUNet model significantly improves the classification of skin lesions. This integration goes beyond traditional paradigms and demonstrates a viable strategy for deploying AI-powered tools in clinical settings.Future workFuture investigations will focus on increasing the version's abilities by using multi-modal imaging information, experimenting with alternative optimization algorithms, and comparing real-world medical applicability. There is also a promising scope for enhancing computational performance and exploring the model's interpretability for more clinical adoption.

Targeting carbon-neutral waste reduction: Novel process design, modelling and optimization for converting medical waste into hydrogen

This study proposes a novel system designed to turn waste masks (WM) into hydrogen while aiming at carbon neutrality. The system utilizes plasma gasification to primarily convert WM into syngas, which is subsequently upgraded through water gas shift to enhance hydrogen fraction. To mitigate carbon emissions, carbon capture techniques including monoethanolamine-based carbon absorption and oxy-fuel combustion are incorporated into the system. A high-fidelity model of the designed process is developed in Aspen Plus. Machine learning-driven optimization is applied to identify the optimal operating conditions. Utilizing Gaussian process regression-based surrogate optimization, the system achieves around 0.5 kg CO2 eq/kg H2 of levelized emissions per unit hydrogen (LEH) in the reaction stage and the execution time required for optimization is only 4 s, outperforming detailed process models-based optimization. Energy assessment highlights plasma gasification and CO2 absorption as the primary sources of energy loss. Furthermore, life cycle analysis indicates that CO2 absorption is the primary contributor to carbon emissions, accounting for approximately 60% of the total emissions. With levelized emissions of 1.27 kg CO2 eq/kg WM and 5.18 CO2 eq/kg H2, the designed system demonstrates superiority over conventional medical waste treatment and hydrogen production methods.

Synergistic cotreatment of cooling tower blowdown and produced waters: Techno-economic, sustainability, and optimization systems analyses

A blowdown and produced water cotreatment train design was preliminarily simulated to explore water-energy nexus synergy. This process model was able to provide results concerning water compositions, chemical demand, and energy intensity. The computational expense associated with this simulation made it intractable to use within optimization algorithms for further informative, comprehensive systems analyses. This work details the efforts of developing a framework suitable for the optimization of computationally demanding wastewater treatment train simulations, specifically demonstrated for the cotreatment train’s process, economic, and sustainability models. The unique challenges of the cotreatment train model motivated a modified surrogate optimization methodology to be developed. Using this framework, worst and best case economic scenarios were optimized to minimize the levelized cost of water (LCOW) employed in a reduced-order model-based simulation. Applying the optimal designs obtained from the surrogate optimization into the original comprehensive simulation resulted in an expected range of LCOW from $2.01 to $4.43m-3. Other established techno-economic and sustainability indicators further inform on process implications. The process was compared to current management practices of blowdown water treatment and produced water injection to discuss the advantages and disadvantages of the proposed cotreatment train designed for water reuse. The cotreatment process was estimated to have lower levelized treatment costs but require more chemicals and energy to manage a higher salinity wastewater than pure blowdown water alone. If sustainable management goals motivate produced water remediation, the cotreatment process can be beneficial for the broader scope of water management.

Reconfigurable Intelligent Surface-Based Backscatter Communication for Data Transmission

Data transmission is one of the critical factors in the future of the Internet of Things (IoT). The techniques of a reconfigurable intelligent surface (RIS) and backscatter communication (BackCom) are in need of a solution of realizing low-power sustainable transmission, which shows great potential in wireless communication. Hence, this paper introduces an RIS-based BackCom system, where the RIS receives energy from a base station (BS) and sends information by backscattering the signals from the BS. To maximize the sum rate of all IoT devices (IoTDs), we jointly optimized the time allocation, the RIS-reflecting phase shifts and the transmit power of the BS by exploiting an alternative optimization algorithm. The simulation results illustrate the effectiveness and the feasibility of the proposed wireless communication scheme and the proposed algorithm in IoT networks.

Deep contextual reinforcement learning algorithm for scalable power scheduling

This article introduces a deep contextual reinforcement learning (DCRL) optimization algorithm to tackle the NP-hard power scheduling problem. The algorithm is based on a multi-agent simulation environment that decomposes the power scheduling problem into sequential Markov decision processes (MDPs). The simulation environment inherently corrects incorrect decisions and adjusts supply capacities so that the MDPs adhere to the optimization constraints. A deep reinforcement learning (DRL) model is trained on these MDPs to provide optimal solutions. Demonstrating its applicability and effectiveness, the proposed method is evaluated on various test systems with different unit numbers, constraints, and production cost functions. The experimental results show that the proposed method has better performance relative to alternative methods, such as binary alternative moth-flame optimization (BAMFO), binary particle swarm optimization (BPSO), teaching learning-based optimization (TLBO), new binary particle swarm optimization (NBPSO), binary learning particle swarm optimization (BLPSO), quasi-oppositional teaching learning-based algorithm (QOTLBO), binary-real-coded genetic algorithm (BRCGA), hybrid genetic-imperialist competitive algorithm (HGICA), three-stage priority list (TSPL), and hybridized evolutionary algorithm and sequential quadratic programming (HEPSQP). The novelties of the proposed method lie in its adaptability to longer planning horizons and linear dimensionality complexity, rendering it suitable for large-scale power systems.

Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification

Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. This study introduces a novel hyperparameter optimization strategy using the Manta Rays Foraging Optimizer (MRFO). A model tailored for skin cancer classification is created by fine-tuning a Convolutional Neural Network (CNN) with MRFO, coupled with in-depth image preprocessing. Empirical evaluations on diverse datasets (ISIC, PH2, HAM10000) showcase the significant superiority of the MRFO-based model over conventional optimization algorithms. The model achieves impressive accuracy and loss metrics (ISIC: 99.43 %, 0.0250; PH2: 99.96 %, 0.0033; HAM10000: 97.70 %, 0.0626), outperforming alternative optimization algorithms such as the Grey Wolf Optimizer (98.33 % accuracy, 0.17 loss), Whale Optimization Algorithm (96 % accuracy), Grasshopper Optimization Algorithm (97.2 % accuracy), Densnet121-MRFO (99.26 % accuracy), InceptionV3 with GA (99.9 % accuracy), and African Vulture Optimization Algorithm (92.7 % accuracy). The novel approach demonstrates superior accuracy and loss metrics, underscoring its potential for precise and efficient skin cancer detection. Additionally, narrow confidence intervals and balanced precision-recall confirm the model’s generalizability and effectiveness, paving the way for early and accurate skin cancer detection and potentially improving patient outcomes.

Scattered storage for retail e-commerce fulfillment warehouses with consideration for product turnover

Storage assignment policy of a warehouse controls how the stocks are assigned to various storage locations. Traditionally used storage policies, such as dedicated or random storage, do not address the specific needs of retail e-commerce fulfillment warehouses that store a wide assortment of products and service a large number of single-unit orders under strict timelines. This has led to the development of scattered storage assignment policy, which deliberately spreads the stocks of every product across the warehouse. However, the available approaches towards scattering have largely ignored product turnover, which is a significant determinant of order picking travel. To fill this research gap, we propose a new, turnover-incorporated scattered storage policy. More specifically, we develop a new measure of scattering of stocks and a mathematical model that optimizes the measure by scattering the stocks across the warehouse, while considering product turnover. Certain important properties of this measure are proved and eventually exploited in developing a heuristic approach to solve the proposed mathematical model for large problem instances. An upper bound for the objective function of the mathematical model is used as a benchmark to evaluate the performance and computation time of the proposed heuristic, as well as solutions obtained through genetic algorithms and surrogate optimization. We conduct a simulation study using the proposed heuristic to analyze the impact of the scattering of stocks on order picking performance in a single block, low level, picker-to-parts warehouse. We also present the impact of warehouse operating parameters on the performance of the proposed scattered storage policy.

Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization

This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning. A new design problem is formulated where the topology optimization hyperparameters are defined as design variables and the problem is solved by surrogate optimization. The new design problem is nested, such that a topology optimization problem is solved as an inner problem. To encourage the identification of high-performing solutions while limiting the computational resource requirements, the outer objective function is defined as the original objective combined with penalization for intermediate densities and deviations from the prescribed material consumption. The contribution is demonstrated on density-based topology optimization with various hyperparameters and objectives, including compliance minimization, compliant mechanism design, and buckling load factor maximization. Consistent performance is observed across all tested examples. For a simple two hyperparameter case, the new framework is shown to reduce amount of times a topology optimization algorithm is executed by 90% without notably sacrificing the objective compared to a rigorous manual grid search.

Distributed Joint Beamforming for RIS-assisted Cell-Free MIMO with Discrete Phase Shifts

Abstract Both reconfiurable intelligent surface (RIS) and cell-free multiple-input multiple-output (CF-MIMO) system are holding promise to become the predominant research direction in 6G networks. This paper studies the problem of weighted sum-rate maximization(WSR) in RIS-assisted CF-MIMO networks with discrete phase shifts. We utilize alternating optimization (AO) to iteratively calculate active and passive beamforming. At access points (APs) side, we employ a distributed algorithm to design the active beamforming, leveraging information interact among APs without centralized designing at central processing unit(CPU). Facing the constraint of discrete phase shift in RIS, we adopt semidefinite relaxation(SDR) projection and proximal gradient descent(PGD) to design the passive beamforming respectively. Simulation results indicate that our proposed algorithms achieve performance comparable to the distributed framework with continuous phase shift while reducing the computational time.

Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system

With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.

EXPRESS: A Machine Learning Approach to Solve the E-commerce Box-Sizing Problem

E-commerce packages are notorious for their inefficient usage of space. More than one-quarter volume of a typical e-commerce package comprises air and filler material. The inefficient usage of space significantly reduces the transportation and distribution capacity increasing the operational costs. Therefore, designing an optimal set of packaging box sizes is crucial for improving efficiency. We present the first learning-based framework to determine the optimal packaging box sizes. In particular, we propose a three-stage optimization framework that combines unsupervised learning, reinforcement learning, and tree search to design box sizes. The package optimization problem is formulated into a sequential decision-making task called the box-sizing game. A neural network agent is then designed to play the game and learn heuristic rules to solve the problem. In addition, a tree-search operator is developed to improve the performance of the learned networks. When benchmarked with company-based optimization formulation and two alternate optimization models, we find that our ML-based approach can effectively solve large-scale problems within a stipulated time. We evaluated our model on real-world datasets supplied by a large e-commerce platform. The framework is currently adopted by a large e-commerce company across its 28 fulfillment centers, which is estimated to save the company about 7.1 million USD annually. In addition, it is estimated that paper consumption will be reduced by 2080 metric tons and greenhouse gas emissions by 1960 metric tons annually. The presented optimization framework serves as a decision support tool for designing packaging boxes at large e-commerce warehouses.

GU-Net: Causal relationship-based generative medical image segmentation model

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms. This leads to a relatively singular training approach for the models, and furthermore, some networks can only perform segmentation for specific diseases. In this paper, we propose a causal relationship-based generative medical image segmentation model named GU-Net. We integrate a counterfactual attention mechanism combined with CBAM into the decoder of U-Net as a generative network, and then combine it with a GAN network where the discriminator is used for backpropagation. This enables alternate optimization and training between the generative network and discriminator, enhancing the expressive and learning capabilities of the network model to output prediction segmentation results closer to the ground truth. Additionally, the interaction and transmission of information help the network model capture richer feature representations, extract more accurate features, reduce overfitting, and improve model stability and robustness through feedback mechanisms. Experimental results demonstrate that our proposed GU-Net network achieves better segmentation performance not only in cases with abundant data samples and relatively simple segmentation targets or high contrast between the target and background regions but also in scenarios with limited data samples and challenging segmentation tasks. Comparing with existing U-Net networks with attention mechanisms, GU-Net consistently improves Dice scores by 1.19%, 2.93%, 5.01%, and 5.50% on ISIC 2016, ISIC 2017, ISIC 2018, and Gland Segmentation datasets, respectively.

An alternative chlorine-assisted optimization of CdS/Sb2Se3 solar cells: Towards understanding of chlorine incorporation mechanism

The current strategies in the development of Sb2Se3 thin film solar cells involve fabrication and optimization of superstrate and substrate device architectures, with the preferable choice for TiO2 and CdS heterojunction layers. For CdS-based superstrate cells, several studies reported the necessity to apply CdCl2 or other metal halide-based post-deposition treatment (PDT), highlighting improvement of CdS/Sb2Se3 device efficiency. However, the need, effect, and mechanism of such PDT are very often not described. Additionally, the fact that many groups have not succeeded in demonstrating its benefits suggests that this strategy is not straightforward, requiring a deeper understanding towards a more unified concept. The present study proposes an alternative approach to the challenging CdCl2 PDT of CdS in CdS/Sb2Se3 device, involving controllable Cl incorporation in CdS films by systematically varying the concentration of NH4Cl in the CBD precursor solution from 1 to 8 mM. Structural and electrical characterizations are correlated with advanced measurements of Scanning Kelvin Probe, surface photovoltage, and atomic force microscopy to understand the impact of Cl incorporation on the properties of CdS films and CdS/Sb2Se3 devices. The validity of Cl incorporation in the CdS lattice and interdiffusion processes at the CdS-Sb2Se3 interface is confirmed by secondary ion mass spectrometry analysis. It is demonstrated that incorporation of 1 mM of NH4Cl, as a Cl source in CBD CdS, can boost the PCE of CdS/Sb2Se3 by ∼20 %. With this approach, we offer new perspectives on the optimization methodology for Cl-based CdS/Sb2Se3 device processing and complementary understanding of the physiochemistry behind these processes.

Energy Minimization for IRS-Assisted SWIPT-MEC System.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.