Optimal Allocation Scheme Research Articles

Deep Learning Framework for Two-Way MISO Wireless-Powered Interference Channels

In this paper, we present a realistic and novel protocol for two-way communication in multiple-input-single-output (MISO) wireless-powered interference channels, namely, a simultaneous wireless information and power transfer (SWIPT) then wireless information transfer (WIT) protocol. In the protocol considered, transmitters first perform SWIPT in a forward link (FL) and receivers and then execute WIT using the harvested energy in a backward link (BL). Given that the operation of SWIPT in the FL affects the performance of WIT in the BL, our aim is to find a resource allocation strategy that maximizes the sum spectral efficiency (SE) of the FL and BL, which requires the joint optimization of the transmit beamforming vector, transmit power, and energy harvesting (EH) ratio in the FL and the receive beamforming vector in the BL. To deal with the non-convexity of the optimization problem, a deep learning (DL) framework is devised, in which the optimal resource allocation strategy is approximated by a well-designed deep neural network (DNN) model consisting of six independent DNN modules where the sigmoid and softmax functions are jointly utilized to properly model each control parameter. Furthermore, a two-stage training method is proposed where the DNN model is initialized using suboptimal solutions that are found using a low complexity algorithm in a supervised manner before it is fine-tuned using the main training based on unsupervised learning. Through intensive simulations performed in various environments, we confirm that the proposed method improves the training performance of the DNN model while reducing the training overhead. As a result, the proposed DL-based resource allocation achieves a near-optimal performance in terms of the sum SE with a low computation time.

Optimal Allocation Strategy of Virtual Inertia and Damping for Improving the Small-signal Stability

The application of the virtual synchronous generator (VSG) control technology to engage the inertia response of renewable energy generation systems is an effective means of coping with the problem of weak inertia in power systems. To deal with the problem of small-signal stability in the weak inertia power system, this paper studies how the strength of virtual inertia and damping affect the small-signal stability, and proposes an optimal allocation strategy of virtual inertia and damping for improving the small-signal stability. The optimal allocation model of virtual inertia is constructed to minimize the energy imbalance of the system under small-signal by selecting the virtual inertia and damping as optimization variables. Regarding the aforementioned model, the evolutionary algorithm is used to obtain the optimal allocation of virtual inertia and damping. Simulations on a virtual synchronized microgrid system show the effectiveness of the proposed strategy in improving small-signal stability.

Research on Multi UAVS Task Allocation Method for Post Disaster Emergency Goods Distribution

When a natural disaster occurs, ensuring timely and efficient delivery of emergency supplies to every affected location is crucial in mitigate the losses caused by the disaster. This study focuses on the distributing emergency supplies to heterogeneous multi-drone (UAV) swarms in the post-disaster scenario. Given the known distribution stations and disaster locations, a model is proposed that enables multiple drones to distribute various emergency supplies to multiple disaster zones. The model incorporates multiple objective constraints, including UAV range, priority of emergency goods, and UAV losses, resulting in a multi-objective optimization problem. To enhance the solving ability of genetic algorithm, we improve genetic algorithm by introducing adaptive cross operators, and its feasibility is compared with traditional genetic algorithms. Simulation experiments demonstrate that the improved genetic algorithm is significantly superior to traditional genetic algorithms in solving the optimal allocation scheme, thus providing a scientific basis for decision-making in emergency supply distribution.

The optimal decision of e-retailer based on return-freight insurance – considering the loss aversion of customers

PurposeThe return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.Design/methodology/approachThe authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.FindingsIt was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.Originality/valueThe authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

A two-stage stochastic programming approach for reserving and allocating of emission trading permits under uncertainties

ABSTRACT The Emissions Trading Scheme (ETS) is a market-based approach aiming at reducing greenhouse gas (GHG) emissions. It involves the allocation of emission allowances to entities that are permitted to emit certain levels of GHG, as well as the re-sale and re-allocation of these allowances among entities. Considering the uncertainty of urban traffic congestion, the problem of reserving and allocating Emission Trading Permits (ETP) under stochastic demand is investigated. The ETP reserve of each link is determined before congestion, the optimal allocation scheme of ETP is determined after congestion. A two-stage stochastic programming model is formulated to minimize the sum of the reserve cost of ETP before congestion and the expected total loss of ETP after congestion. First, the effectiveness of the stochastic programming model is verified by comparing with the traditional method. Second, the total cost increases with the increase of initial reserve cost and shortage cost, and decreases with the increase of selling price and the capacity of links by sensitivity analysis. The unit reserve cost has a negative effect on the reserve quantity, while shortage cost, selling price, and link capacity have a positive effect on the reserve quantity. At last, the validity of the stochastic programming solution can be verified by numerical analysis of the Nguyen-Dupuis network. The stochastic programming solution proposed in this study shows an 89% reduction in ETP reserve compared to the optimal initial capacity solution. When level E occurs, the total cost is correspondingly reduced by 11%.

Optimal battery energy storage planning and control strategy for grid modernization using improved genetic algorithm

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS’s optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid’s energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability.

Multi-Objective Optimal Scheduling of Generalized Water Resources Based on an Inter-Basin Water Transfer Project

For inter-basin water transfer (IBWT) projects, the conflict between social, economic, and ecological objectives makes water allocation processes more complex. Specific to the problem of water resource conflict in IBWT projects, we established an optimal allocation model of generalized (conventional) water resources (G (C) model) to demonstrate the advantages of the G model. The improved multi-objective cuckoo optimization algorithm (IMOCS) was applied to search the Pareto frontiers of the two models under normal, dry, and extremely dry conditions. The optimal allocation scheme set of generalized (conventional) water resources (G (C) scheme set) consists of ten Pareto optimal solutions with the minimum water shortage selected from the Pareto optimal solutions of the G (C) model. The analytic hierarchy process (AHP) combined with criteria importance using the inter-criteria correlation (CRITIC) method was used to assign weights of evaluation indexes in the evaluation index system. The non-negative matrix method was employed to evaluate the G (C) scheme set to determine the best G (C) scheme for the Jiangsu section of the South-to-North Water Transfer (J-SNWT) Project. The results show that (1) the Pareto frontier of the G model is better than that of the C model, and (2) the best G scheme shows better index values compared to the best C scheme. The total water shortages are reduced by 254.2 million m3 and 827.9 million m3 under the dry condition, respectively, and the water losses are reduced by 145.1 million m3 and 141.1 million m3 under the extremely dry condition, respectively. These findings could not only provide J-SNWT Project managers with guidelines for water allocation under normal, dry, and extremely dry conditions but also demonstrate that the G model could achieve better water-allocation benefits than the C model for inter-basin water transfer projects.

Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency

Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.

Role Mechanism of Enterprise Management Efficiency Improvement Based on Improved Drosophila Algorithm

INTRODUCTION: With today's intelligent technology in full swing, many companies and enterprises need to catch up to the reality of internal management. To make the company better adapt to society and realize its sustainable development, it is essential to optimize the internal management means and improve the management efficiency of the enterprise.
 OBJECTIVES: To find the optimal staff allocation scheme and the best decision path by utilizing the improved fruit fly algorithm and establishing the enterprise's regular task and staff allocation model.
 METHODS: We analyze the standard swarm intelligence algorithms, and then we compare the differences between the basic fruit fly algorithm, the optimized fruit fly algorithm, and the above swarm intelligence algorithms. The fruit fly algorithm is utilized, and the algorithm is optimized to fit the actual enterprise management model. At the same time, the influence of Levy's flight on the Drosophila algorithm in enterprise management efficiency improvement is studied. Finally, it points out the application fields, the optimized Drosophila algorithm's current situation, and the existing problems and shortcomings.
 RESULTS: By comparing the performance of the Drosophila algorithm with that of the other three optimization Drosophila algorithms, the influence factors of Levy flight on the enterprise's internal management allocation are obtained, making the algorithm a good fit for the model. The results of the optimal allocation scheme under known conditions and the best decision were obtained.
 CONCLUSION: The experimental results show that the optimized fruit fly algorithm can solve the multi-parameter allocation problem of enterprise management, which has high reference value and significance for general enterprise internal management.

An improved bi-level programming model for water resources allocation under multiple uncertainties

Nowadays, with the scarcity of water resources, competition for water resources among different levels and water sectors is growing increasingly fierce. Furthermore, uncertainties are unavoidable in the water resources system. To address the aforementioned issues, a fuzzy max-min decision bi-level multi-objective interval programming model was proposed, which can not only focus on water conflicts at the same level or between different levels, but also pay attention to optimal allocation of water resources under uncertainty. The developed model was then applied to a case study in Wuwei City, Gansu Province, China, which selected fairness of water distribution and agricultural economic benefits as planning objectives. Based on the developed model, different water resources optimal allocation schemes under different representative hydrological years were provided. From the result, as representative hydrological years changed from wet (P = 25%) to dry (P = 75%), agricultural economic benefit and Gini coefficient of agriculture would vary from [35.19, 37.78] × 108 yuan to [31.12, 31.99] × 108 yuan and from [0.468, 0.429] to [0.505, 0.503], which indicates that as available water resources decrease, agricultural economic benefit would decrease and fairness of water distribution would also decrease. And the water distribution fairness of the upper bound water allocation scheme is higher than that of the lower bound water allocation scheme when in the same representative hydrological year. In addition, no matter what representative hydrological year, the results of the established bi-level programming model are always in the middle of the results of the upper and lower level individual objective, which means that the developed bi-level programming model has great advantage to deal with water competing conflict among different levels. Furthermore, based on the results of developed model, the reasonable water resources optimization schemes can be determined by the decision-makers when faced with multi-objective, bi-level and multiple uncertainties problems.

Cooperative-game-based joint planning and cost allocation for multiple park-level integrated energy systems with shared energy storage

Under the carbon-neutrality goal, joint planning along with a fair cost allocation of shared energy storage becomes a promising solution to boosting the economic benefits and energy utilization efficiency of multiple park-level integrated energy systems. Hence, a joint planning and cost allocation method for multiple park-level integrated energy systems with shared energy storage is proposed in this paper to obtain optimal joint planning and cost allocation strategies of park-level integrated energy systems with shared energy storage. First, a joint planning model for park-level integrated energy systems with shared energy storage is established to minimize the total investment and operation costs of the grand cooperative coalition by considering the optimal construction time sequence of devices. Then, an ex-post cost allocation algorithm based on an improved Nucleolus method is proposed to realize a fair cost allocation for the joint planning of park-level integrated energy systems with shared energy storage. Finally, case studies on a grand cooperative coalition with six park-level integrated energy systems are performed for verifying the effectiveness of the proposed method. Simulation results demonstrate that the total investment and operation costs of park-level integrated energy systems are reduced, and the renewable energy consumption levels of park-level integrated energy systems are improved. Besides, the proposed cost allocation algorithm has less computing time, and better coalition rationality and stability compared to other similar algorithms.

Managing Water-Energy-Carbon Nexus for Urban Areas With Ambiguous Moment Information

The rapid growth of electric load fuels urbanization but creates unsettling environmental challenges. In that regard, the energy system is a crucial part of consuming water and emitting greenhouse gases. To support reliable and economical energy system operations, with reduced water waste and carbon emissions, we design a viable co-optimization of water-energy-carbon nexus for an integrated energy system by adequately modelling essential interdependencies of power, gas, and water systems. A two-stage distributionally robust optimization method is proposed: the first-stage model minimizes the day-ahead reserve capacity scheduling for the next day, and the second-stage model enables real-time dispatch by considering the variability in renewable power generation. The carbon emissions by power generation and distribution are incorporated in both stages to ensure low-carbon operations. We use a two-stage moment-based distributionally robust approach to capture and represent the uncertainties in renewable generation. Instead of assuming a deterministic distribution, we characterize an ambiguity set with mean vectors and covariance matrices, which produces a family set of distributions. We conduct case studies using a modified IEEE 33-bus power system that includes and links a 20-node gas system and a 10-node water system. The comparative results indicate that the proposed co-optimization of water-energy-carbon nexus is superior than several benchmarks. By considering the close interdependencies of water, energy, and carbon, we create an optimal resource allocation scheme, which is capable of minimizing operation costs, lessening carbon emissions, and reducing water waste. The current research enables effective operation schemes for urban energy systems, illustrates a viable way to curb local environmental emissions, and increases water usage efficiency in the water-energy-carbon nexus.

Optimizing Worst Case Data Freshness in RF-Powered Networked Embedded Systems

Maintaining real-time data freshness plays a critical role in ensuring system correctness and optimizing the system performance in networked embedded systems (NESs). To quantitatively measure the freshness of the collected real-time data, the concept of Age of Information (AoI) has been extensively studied in recent years. This article explores how to minimize the worst-case AoI of real-time data in RF-powered NESs. In such systems, one hybrid access point (HAP) transfers wireless power to a set of distributed sensor nodes, and in the meantime, receives the information from these sensor nodes. We utilize the metric of AoI to measure the data freshness and present a comprehensive analysis of the worst-case AoI of the real-time data in the target system. Based on the analysis, an optimal energy schedule solution is designed to judiciously determine individual sensor nodes’ energy and time allocation to minimize the worst-case AoI. Considering the varying importance of different information and sensor nodes in the target system, we further propose the optimal time and energy allocation scheme for minimizing the weighted worst-case AoI. A multi-node RF-powered NES testbed is implemented to validate the functional correctness of our solutions. The results show that our solutions significantly outperform the state-of-the-art solutions, reducing the worst-case AoI and weighted worst-case AoI by 69.3% and 75.1% on average, respectively.

Optimal configuration for the wind-solar complementary energy storage capacity based on improved harmony search algorithm

With the increase in the permeability of renewable energy, the randomness and uncertainty of photovoltaic power generation and wind power generation have an impact on the stable operation of the power grid. Energy storage technology can effectively improve the controllability of new energy, but the optimal allocation of energy storage capacity has always been a difficult problem limiting the development of energy storage technology. In this paper, the capacity optimization model of the complementary energy storage system is established based on the analysis of the wind-solar energy storage principle and the energy balance criterion. Then, with the minimum investment cost, the highest economic benefit, low wind and light abandonment rate and low load power shortage rate as the optimization objectives, the harmony search algorithm was used to solve the optimization model under the constraint of stable operation of the system, and the optimal capacity allocation scheme was obtained. The results of the example show that the optimization strategy in this paper can reduce the investment cost of the system and improve the reliability of the power supply and energy efficiency of the system.

Research on the capacity allocation optimization of micro-grid considering wind-PV-ES

To achieve the goal of “2030 carbon peak and 2060 carbon neutralization” and optimize the form of multi-energy utilization in the industrial park, it is very important to fully exploit distributed clean energy and micro-grid energy system in the park. In view of the economy of micro-grid investment, the capacity proportion of wind, solar and energy storage is the key point in the planning and design stage. A capacity allocation optimization strategy of micro-grid considering wind/solar/thermal/energy storage is proposed. The model takes the minimum total cost of the system as the objective function. As the characteristics of load in the industrial park has a poor adjust ability, the energy storage is added to the integrated energy allocation system as a flexible adjustable power supply. The energy capacity allocation of micro-grid in a cement plant park in China is analysed and calculated in this paper. The results show that after the completion of wind power, photovoltaic and thermal power, the annual power generation of the project will increase by about 105540 MWh, save about 31000 tons of standard coal per year, and reduce about 84000 tons of carbon dioxide emissions. As the cost of renewable energy will be further reduced under the sustainable development of clean energy and the trend of future technology development, establishing a clean energy micro-grid in the industrial park can bring considerable economic benefits. This study can provide a reference for the capacity allocation of the energy system in the industrial park at the planning stage.

Study of the allocation strategy of water pollutant emission permits under a bidirectional ecological compensation mechanism

Water pollutant emission permits (WPEPs) allocation is a competing limited consumption resource designed to address the issue of reconciling water pollution with economic development. In the wake of increasing conflicts, a pressing challenge is how to motivate stakeholders associated with the basin to establish a balance between economic development and environmental preservation. Based on the unidirectional flow of basin pollution, this study established a bidirectional ecological compensation (BEC) mechanism, which incorporated the key water pollution indicators (WPEPs allocation volume) into the BEC mechanism. By integrating the BEC mechanism with the WPEPs allocation model, a multi-objective optimization framework for balancing the fairness and economic efficiency of WPEPs allocation under the BEC synergy mechanism has been proposed. The Tuojiang river basin in China was selected for the study, and the allocation results and ecological compensation mechanisms under different allocation preferences were analyzed to verify the validity and practicality of the model. Results revealed that (1) BEC mechanisms can be considered to ensure equitable and efficient allocation of WPEPs in the basin. (2) Under the perspective of watershed cooperation, the bidirectional compensation mechanism proves to be an effective measure to promote economic development and environmental protection of the watershed. (3) Preferences of watershed managers for allocation and ecological compensation mechanisms can have an impact on the outcomes of allocation and ecological compensation mechanisms. This study provides reference for effective management and adoption of optimal allocation strategy of watershed WPEPs and synergistic management of BEC mechanisms.

Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework

A novel newsvendor model-based framework for regional industrial water resources allocation that considers uncertainties in water supply and demand was proposed in this study. This framework generates optimal water allocation schemes while minimizing total costs. The total cost of water allocation consists of the allocated water cost, the opportunity loss for not meeting water demand, and the loss of the penalty for exceeding water demand. The uncertainties in water demand and supply are expressed by cumulative distribution functions. The optimal water allocation for each water use sector is determined by the water price, the unit loss of the penalty and opportunity loss, and the cumulative distribution functions. The model was then applied to monthly water allocation for domestic, industrial, and agricultural water use in two counties of Huizhou City, China, whose water supply mainly depends on Baipenzhu Reservoir. The water demand for each water use sector and the monthly reservoir inflow showed good fits with the uniform and P-III distributions, respectively. The water demand satisfied ratio for each water use sector was stable and increased for the optimal water allocation scheme from the newsvendor model-based framework, and the costs were lower compared with the actual water allocation scheme. The novel framework is characterized by less severe water shortages, lower costs, and greater similarity to actual water use compared with the traditional deterministic multi-objective analysis model, and demonstrates strong robustness in the advantages of lower released surplus water and higher water demand satisfied ratio. This novel framework yields the optimal water allocation for each water use sector by integrating the properties of the market (i.e., determining the opportunity loss for not meeting water demand) with the government (i.e., determining the water price and the loss of the penalty for exceeding water demand) under the strictest water resources management systems.

Predictive simulation and optimal allocation of surface water resources in reservoir basins under climate change

Predicting and allocating surface water resources are becoming increasingly important tasks for addressing the risk of water shortages and challenges of climate change, especially in reservoir basins. However, surface water resource management includes many systematic uncertainties and complexities that are difficult to address. Thus, advanced models must be developed to support predictive simulations and optimal allocations of surface water resources, which are urgently required to ensure regional water supply security and sustainable socioeconomic development. In this study, a soil and water assessment tool-based interval linear multi-objective programming (SWAT-ILMP) model was developed and integrated with climate change scenarios, SWAT, interval parameter programming, and multi-objective programming. The developed model was applied to the Xinfengjiang Reservoir basin in South China and was able to identify optimal allocation schemes for water resources under different climate change scenarios. In the forecast year 2025, the optimal water quantity for power generation would be the highest and account for ∼60% of all water resources, the optimal water quantity for water supply would account for ∼35%, while the optimal surplus water released from the reservoir would be the lowest at ≤5%. In addition, climate change and reservoir initial storage would greatly affect the surplus water quantity but not the power generation or water supply quantity. In general, the SWAT-ILMP model is applicable and effective for water resource prediction and management systems. The results from different scenarios can provide multiple alternatives to support rational water resource allocation in the study area.

Spatiotemporal vaccine allocation policies for epidemics with behavioral feedback dynamics

AbstractMotivated by the COVID‐19 pandemic, we study how a public health authority may allocate vaccines from a limited stockpile to different jurisdictions over time. We propose an epidemiological model with time‐varying contact rates determined by a stylized behavioral feedback mechanism to reflect multi‐wave transmission dynamics. We evaluate the performance of various information‐sensitive allocation policies (e.g., allocation proportional to local incidence) as alternatives to the widely used pro‐rata policy. We also obtain optimized allocation strategies under the proposed epidemiological model with fairness and implementable freeze‐period constraints. For the case of a multi‐wave epidemic as represented by our compartmental model with behavioral feedback, we find that none of the alternative policies offers consistently more efficient allocations than a simple pro‐rata policy across a broad range of behavioral parameter settings. In fact, in some cases the alternative policies may actually result in less efficient allocations than the pro‐rata policy. Thus our results support the conclusion that the widely used pro‐rata policy can be well justified because it is simple to explain/implement and does not cause unexpected adverse effects. However, if policy makers are willing to invest in more tailored strategies based on numerical optimization, then the identified optimized strategies are a more favorable option as they allow for a more efficient allocation of vaccines.

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.