Multi-objective Task Research Articles

Metaverse Age: Scheduling Strategies for Digital Resource Management

The Metaverse is a new Internet application and social form: virtual and real integration of various new technologies. This paper explores optimal strategies for Digital Resource Management (DRM) and Multi-objective Task Scheduling (MTS) in the Metaverse era. To this end, it explores the correlation between the Metaverse and the Internet of Things (IoT). The Metaverse's operation & control and the DRM and schedules are further elaborated. Finally, the next-generation interaction between humans and the Metaverse is outlooked. Thus, it provides a reference for the potential development of DRM and scheduling in the Metaverse era.

Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling.

Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS) in manufacturing. Genetic programming (GP), as a popular hyperheuristic approach, has been used to learn scheduling heuristics for generating schedules for multitask single-objective DFJSS only. Searching in the heuristic space with GP is more difficult than in the solution space, since a small change on heuristics can lead to ineffective or even infeasible solutions. Multiobjective DFJSS is more challenging than single DFJSS, since a scheduling heuristic needs to cope with multiple objectives. To tackle this challenge, we first propose a multipopulation-based multitask multiobjective GP algorithm to preserve the quality of the learned scheduling heuristics for each task. Furthermore, we develop a multitask multiobjective GP algorithm with a task-oriented knowledge-sharing strategy to further improve the effectiveness of learning scheduling heuristics for DFJSS. The results show that the designed multipopulation-based GP algorithms, especially the one with the task-oriented knowledge-sharing strategy, can achieve good performance for all the examined tasks by maintaining the quality and diversity of individuals for corresponding tasks well. The learned Pareto fronts also show that the GP algorithm with task-oriented knowledge-sharing strategy can learn competitive scheduling heuristics for DFJSS on both of the objectives.

A novel dynamic multi-objective task scheduling optimization based on Dueling DQN and PER

A novel dynamic multi-objective task scheduling optimization based on Dueling DQN and PER

Deep reinforcement learning for a multi-objective operation in a nuclear power plant

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.

Three-step learning strategy for designing 15Cr ferritic steels with enhanced strength and plasticity at elevated temperature

15Cr ferrite steels are urgently required in advanced Ultra-supercritical power plants but meet design challenges in balancing excellent strength and plasticity at high temperatures. We developed a three-step learning strategy based on mutually driven machine learning and purposeful experiments to complete this multi-objective task. Compared with traditional adaptive learning and local-interpolation learning, this step-by-step modular manner provides good transparency and interpretability of the information flow, which is ensured by identifying essential factors from an exquisitely prepared composition-microstructure dataset, and learning valuable knowledge about the composition-property relationship. The requirement of only two groups of experiments indicates the low cost and high efficiency of the strategy. Performing the strategy, we found that Ti is another key element affecting the Laves phase besides Mo and W, and their effects on ultimate tensile strength (UTS) and elongation were also uncovered. Importantly, several low-cost steels free of Co were successfully designed, and the best steel exhibited 156%, 31%, and 62% higher UTS and elongation at 650 °C than the typical 9Cr, 15Cr, and 20Cr steels, respectively. Based on the advantages and success of the strategy in terms of alloy improvement, we believe the strategy suits other multi-objective design tasks in more materials systems.

A Two-Stage Multi-Objective Task Scheduling Framework Based on Invasive Tumor Growth Optimization Algorithm for Cloud Computing

A Two-Stage Multi-Objective Task Scheduling Framework Based on Invasive Tumor Growth Optimization Algorithm for Cloud Computing

Constructing ensembles of dispatching rules for multi-objective tasks in the unrelated machines environment

Scheduling is a frequently studied combinatorial optimisation problem that often needs to be solved under dynamic conditions and to optimise multiple criteria. The most commonly used method for solving dynamic problems are dispatching rules (DRs), simple constructive heuristics that build the schedule incrementally. Since it is difficult to design DRs manually, they are often created automatically using genetic programming. Although such rules work well, their performance is still limited and various methods, especially ensemble learning, are used to improve them. So far, ensembles have only been used in the context of single-objective scheduling problems. This study aims to investigate the possibility of constructing ensembles of DRs for solving multi-objective (MO) scheduling problems. To this end, an existing ensemble construction method called SEC is adapted by extending it with non-dominated sorting to construct Pareto fronts of ensembles for a given MO problem. In addition, the algorithms NSGA-II and NSGA-III were adapted to construct ensembles and compared with the SEC method to demonstrate their effectiveness. All methods were evaluated on four MO problems with different number of criteria to be optimised. The results show that ensembles of DRs achieve better Pareto fronts compared to individual DRs. Moreover, the results show that SEC achieves equally good or even slightly better results than NSGA-II and NSGA-III when constructing ensembles, while it is simpler and slightly less computationally expensive. This shows the potential of using ensembles to increase the performance of individual DRs for MO problems.

Task Scheduling for Distributed AUV Network Target Hunting and Searching: An Energy-Efficient AoI-Aware DMAPPO Approach

In this article, we aim to design a task scheduling scheme for the underwater multi-objective task of target hunting and environmental search. A distributed autonomous underwater vehicle (AUV) network is deployed to perform the task, where AUVs equipped with sensors can cooperatively search the environment and hunt the target by sharing local information. To achieve efficient exploration of the overall environment by the AUV network, we design an intra-network cooperative searching approach based on the age of information (AoI). Besides, it is critical to conceive an energy-efficient mechanism due to the energy constraints of AUVs and the difficulty of sustainable energy supply. To address the aforementioned issues, we propose an energy-efficient distributed multi-agent proximal policy optimization (DMAPPO) scheme to perform real-time AUV target hunting and environment searching in underwater turbulent fields. The proposed scheme can adjust the number of AUVs assigned to each objective according to practical requirement and residual energy. Distributed AUVs can make decisions autonomously and cooperatively complete the task efficiently through limited information interaction. In addition, we derive a lower bound on the policy improvement of MAPPO. Moreover, our simulation results demonstrate that the proposed scheme outperforms the standard algorithms in terms of hunting efficiency, degree of searching, and network energy efficiency.

Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

With the advent of the information age, the iterative speed of software update is gradually accelerating which makes software development severely limited by software testing. Test case prioritization is an effective way to accelerate software testing progress. With the introduction of heuristic algorithm to this task, the processing efficiency of test cases has been greatly improved. However, to overcome the shortcomings of slow convergence speed and easy fall into local optimum, the improved whale optimization algorithm is proposed for test case prioritization. Firstly, a model called n-dimensional directed search space is established for the swarm intelligence algorithm. Secondly, the enhanced whale optimization algorithm is applied to test case prioritization while the backtracking behavior is conducted for individuals when hitting the wall. In addition, a separate storage space for Pareto second optimization is also designed to filter the optimal solutions of the multi-objective tasks. Finally, both single-objective and multi-objective optimization experiments are carried out for open source projects and real-world projects, respectively. The results show that the improved whale optimization algorithm using n-dimensional directed search space is more conducive to the decisions of test case prioritization with fast convergence speed.

M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.

A heuristic multi-objective task scheduling framework for container-based clouds via actor-critic reinforcement learning

A heuristic multi-objective task scheduling framework for container-based clouds via actor-critic reinforcement learning

Utilization and criticality based fault-tolerant scheduling in multicore mixed critical systems

PurposeThe main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main objectives improving schedulability, achieving reliability and minimizing the number of cores used. The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.Design/methodology/approachThe paper opted for a simulation-based study. The simulation of mixed critical applications, like air traffic control systems and synthetic workloads, is carried out using a litmus-real time testbed on an Ubuntu machine. The heuristic algorithms for task allocation based on utilization factors and task criticalities are proposed for partitioned approaches with multiple objectives.FindingsBoth partitioned earliest deadline first (EDF) with the utilization-based heuristic and EDF-virtual deadline (VD) with a criticality-based heuristic for allocation works well, as it schedules the air traffic system with a 98% success ratio (SR) using only three processor cores with transient faults being handled by the active backup of the tasks. With synthetic task loads, the proposed criticality-based heuristic works well with EDF-VD, as the SR is 94%. The validation of the proposed heuristic is done with a global and partitioned approach of scheduling, considering active backups to make the system reliable. There is an improvement in SR by 11% as compared to the global approach and a 17% improvement in comparison with the partitioned fixed-priority approach with only three processor cores being used.Research limitations/implicationsThe simulations of mixed critical tasks are carried out on a real-time kernel based on Linux and are generalizable in Linux-based environments.Practical implicationsThe rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.Originality/valueThis paper fulfills an identified need to have multi-objective task scheduling in a mixed critical system. The timing analysis helps to identify performance risks and assess alternative architectures used to achieve reliability in terms of transient faults.

A Multi-Objective Approach Based on Differential Evolution and Deep Learning Algorithms for VANETs

Intelligent transportation systems (ITS) are becoming more prominent in our society (for example, in smart cities), although a number of challenges remain to be (fully) addressed (e.g., high vehicle mobility). In this paper, we propose a scheme that combines both a cluster algorithm and a Multi-Objective Task Distribution algorithm based on Differential Evolution (MOTD-DE), designed to ensure stability and reliability in vehicular ad-hoc network (VANET) deployments. Specifically, we use Kubernetes container-base as the cluster algorithm to select various vehicles that fulfill the algorithm's conditions. Hence, this allows us to perform complex tasks on behalf of data owner vehicles. In our approach, the vehicles' information will be available on the master vehicle (data owner vehicle) when the vehicle joins the cluster, and a deep learning model is used to define the fit complexity of sub-tasks. The proposed MOTD-DE distributes sub-tasks between vehicle clusters to reduce the execution time and the resources (vehicles) used to perform a task. We also assume the sub-tasks to be independent. To evaluate our work, we propose scenarios with varying number of tasks, vehicles, CPU and memories values, and distances between cluster vehicles and data owner vehicle. A comparative summary of the evaluation findings between MOTD-DE and four other widely used approaches (i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant-Colony algorithm (ACO), and Artificial-Bee-Colony (ABC) algorithm) shows that MOTD-DE outperforms these competing approaches.

Self-Disciplined Nonsmooth Coordination Control for Battery Energy Storage System in Autonomous DC Microgrids Toward Large-Signal Stability

For a Battery Energy Storage System (BESS)-based autonomous DC microgrid, owing to the coupling complexity between multiple control objectives under a hierarchical control framework, coordination control for large-signal stabilization is well-acknowledged as a non-trivial problem. This paper aims to present a self-disciplined nonsmooth coordination control strategy to address the multi-objective task within one framework. Firstly, a nonsmooth composite regulation law is designed to mitigate the increasing constant power loads (CPLs) by employing a feedforward compensation. Secondly, a dynamic droop strategy aiming for dynamic power allocation for state-of-charging (SOC) balancing of BESS is built based on a nonsmooth dynamic average consensus protocol with low communication cost. Then by utilizing a dynamic power allocation strategy and a large-signal stabilizer for each energy storage unit (ESU), both SOC dynamic balancing and system level large-signal stability can be ensured. Moreover, a fast performance recovery ability can be promised by bringing into two convergence rate coordination factors. Simulation and comparative experimental studies have demonstrated the efficacy of the proposed control strategy.

EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework.

Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO's potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique's performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.

Design and validation of a multi-objective waypoint planning algorithm for UAV spraying in orchards based on improved ant colony algorithm.

Current aerial plant protection with Unmanned Aerial Vehicles (UAV) usually applies full coverage route planning, which is challenging for plant protection operations in the orchards in South China. Because the fruit planting has the characteristics of dispersal and irregularity, full-coverage route spraying causes re-application as well as missed application, resulting in environmental pollution. Therefore, it is of great significance to plan an efficient, low-consumption and accurate plant protection route considering the flight characteristics of UAVs and orchard planting characteristics. This study proposes a plant protection route planning algorithm to solve the waypoint planning problem of UAV multi-objective tasks in orchard scenes. By improving the heuristic function in Ant Colony Optimization (ACO), the algorithm combines corner cost and distance cost for multi-objective node optimization. At the same time, a sorting optimization mechanism was introduced to speed up the iteration speed of the algorithm and avoid the influence of inferior paths on the optimal results. Finally, Multi-source Ant Colony Optimization (MS-ACO) was proposed after cleaning the nodes of the solution path. The simulation results of the three test fields show that compared with ACO, the path length optimization rate of MS-ACO are 3.89%, 4.6% and 2.86%, respectively, the optimization rate of total path angles are 21.94%, 45.06% and 55.94%, respectively, and the optimization rate of node numbers are 61.05%, 74.84% and 75.47%, respectively. MS-ACO can effectively reduce the corner cost and the number of nodes. The results of field experiments show that for each test field, MS-ACO has a significant optimization effect compared with ACO, with an optimization rate of energy consumption per meter of more than 30%, the optimization rate of flight time are 46.67%, 56% and 59.01%, respectively, and the optimization rate of corner angle are 50.76%, 61.78% and 71.1%, respectively. The feasibility and effectiveness of the algorithm were further verified. The algorithm proposed in this study can optimize the spraying path according to the position of each fruit tree and the flight characteristics of UAV, effectively reduce the energy consumption of UAV flight, improve the operating efficiency, and provide technical reference for the waypoint planning of plant protection UAV in the orchard scene.

Application of Chaotic Cat Swarm Optimization in Cloud Computing Multi Objective Task Scheduling

Application of Chaotic Cat Swarm Optimization in Cloud Computing Multi Objective Task Scheduling

Multi-Objective Multi-Resource Task Allocation For Collaborative Robots Systems

The shift from Industry 4.0 to Industry 5.0 has led to a greater focus on workers’ needs in the workplace. Collaborative robots have been introduced to promote a fair division of tasks and reduce physical and mental strain on workers. However, there is a lack of research on how to implement human-centered task allocation. This study proposes a model for multi-objective task allocation, including minimizing makespan, energy expenditure, and mental workload. The study also suggests a method for evaluating mental workload. Results show that the strictness of task sequence affects makespan and energy expenditure, and a new constraint related to idle times is proposed. The optimal level of worker saturation is one that minimizes makespan while minimizing increases in energy expenditure and mental workload.

Multi-objective Task Scheduling Optimization Based on Improved Bat Algorithm in Cloud Computing Environment

Multi-objective Task Scheduling Optimization Based on Improved Bat Algorithm in Cloud Computing Environment

IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks

Nowadays, with the advent of various communication technologies such as the internet of things (IoT), a large volume of data is produced that needs to be processed in real‐time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consist of a set of dependent tasks that can be separately processed in a heterogeneous fog environment. Scheduling these tasks in a fog environment is an NP‐hard problem that needs a vast amount of time and computation resources to solve, making it infeasible for real‐time applications. In addition, reducing response time and energy consumption in fog computing is an essential issue that should be taken into account in task scheduling algorithms. To address these challenges, we aim to propose a multiobjective task scheduling model to jointly improve energy efficiency and response time. To solve the model, we also propose an intelligent solution named IETIF which combines and leverages the benefits of simulated annealing and NSGA‐III algorithms. Simulation results show that IETIF outperforms the state‐of‐the‐art methods in terms of energy consumption, response time, and speedup.