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Modified multi-objective firefly algorithm for task scheduling problem on heterogeneous systems

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2016年第6期8卷 379-393页

作者： Eswari, R. Nickolas, S. Natl Inst Technol Dept Comp Applicat Tiruchirappalli 620015 Tamil Nadu India

scheduling an application in a heterogeneous environment to find an optimal schedule is a challenging optimisation problem. Maximising the reliability of the application even when processors fails, adds more complexity to the problem. Both the objectives are conflict in nature, where maximising reliability of the application may increase application's completion time. Meta-heuristic algorithms are playing important role in solving the optimisation problem. In this paper, the applicability and efficiency of the new meta-heuristic algorithm called firefly algorithm to solve the workflow multi-objective task scheduling problem is studied. A modified version of the firefly algorithm (MFA) using weighted sum method and a modified version of multi-objective firefly algorithm (MMOFA) using Pareto-dominance method are proposed to solve the multi-objective task scheduling problem. The simulation results show that the proposed algorithms can be used for producing task assignments and also give significant improvements in terms of generating schedule with minimum makespan and maximum reliability compared with existing algorithms.

关键词： task scheduling problem multi-objective optimisation multi-objective firefly algorithm MOFA modified algorithms

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Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

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Iran Journal of Computer Science 2025年 1-28页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm task scheduling problem

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An enhanced meta-heuristic algorithm used for energy conscious priority-based task scheduling problems in heterogeneous multiprocessor systems

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 43卷

作者： Sahoo, Ronali Madhusmita Padhy, Sasmita Kumari Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Odisha India

The task scheduling problem (TSP) in Multiprocessor Systems (MPS) is an emerging research area in heterogeneous distributed computing environments. Managing complex tasks and achieving optimal efficiency while scheduling tasks in MPS presents challenges. Although adding more processors to a single system greatly increases its processing power, the energy these processors produce is the main drawback. Here, we employed a multi-objective optimization strategy to reduce the makespan and Energy Consumption (EC). To reduce processor EC, we considered an effective Dynamic Voltage and Frequency Scaling (DVFS) technique. Three distinct energy sources are considered, originating from the processors' communication, idle, and active states. The scheduling sequence of tasks and the assignment of tasks to processors are two vital aspects of TSP. Here, the tasks are arranged based on priority using the Upward Rank technique. To minimize the makespan and EC while allocating tasks to processors, we use the population-based metaheuristic method called Enhanced Honey Badger Optimisation (EHBO). We proposed three improvements to the HBO algorithm in EHBO. Initially, we addressed opposite learning-based population initialization to exclude the least suitable candidates and generate a candidate scheduling population that adheres to task precedence. Subsequently, the levy-flight technique is employed to improve the local and global search and preserve their ideal healthy balance. Finally, using dynamic values rather than constant value improves the ability to obtain maximum food. Several experiments are conducted on random task graphs and real-life data sets. Additionally, the results are compared with other upgraded meta- heuristic algorithms, demonstrating the superiority of EHBO.

关键词： task scheduling problem Heterogeneous Multiprocessor System Makespan Energy Consumption Honey Badger Optimization

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AN IMPROVED GRASSHOPPER OPTIMIZATION ALGORITHM FOR task scheduling problemS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第5期15卷 1967-1987页

作者： Zhao, Ran Ni, Hong Feng, Hangwei Song, Yaqin Zhu, Xiaoyong Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr 21North 4th Ring Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn 19 AYuquan Rd Beijing 100049 Peoples R China

Grasshopper Optimization Algorithm (GOA) is a novel meta-heuristic algorithm for optimization problems. GOA is easy to implement but it cannot make full utilization of every iteration, and it is easy to fall into the local optimal. To improve the performance of GOA, an Improved Grasshopper Optimization Algorithm (IGOA) was proposed in this paper. Firstly, the nonlinear comfort zone parameter was used to promote the utilization of the iterations of the algorithm. Then the Levy flight mechanism was applied to increasing the randomness and expanding the local search radius. At last the random jumping strategy was introduced to help the algorithm jump out of the local optimal. Several experiments involving 29 well-known benchmark functions were conducted and the performance of IGOA was compared with the basic GOA, Opposition-Based Learning GOA (OBLGOA), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO). IGOA was also applied to the task scheduling problem in the resource-constrained system. The results of the experiments demonstrated that the proposed IGOA outperformed GOA and other algorithms.

关键词： Grasshopper optimization algorithm Nonlinear parameter Levy flight Random jumping strategy task scheduling problem

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A hybrid genetic-based task scheduling algorithm for cost-efficient workflow execution in heterogeneous cloud computing environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第8期27卷 10833-10858页

作者： Dehnavi, Mohsen Khademi Broumandnia, Ali Shirvani, Mirsaeid Hosseini Ahanian, Iman Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran

Many businesses utilize cost-efficient cloud services to execute their industrial and scientific workflow applications. Business continuity is a very important issue for both cloud users and providers. To have reliable workflow execution, the engagement of reliable resources is a challenging job that can supply business continuity. In addition, the lowest execution time and monetary cost are two cost features that adhere users to providers. In this regard, the task scheduling algorithm is very prominent in reducing costs in favor of users and providers. To address the issue, a system framework and different cost-type models are suggested. Then, the task scheduling issue is formulated into an optimization problem with an overall cost reduction viewpoint. To solve this NP-Hard problem, a hybrid genetic algorithm (HGA) is presented for reliable and cost-efficient task scheduling of workflow execution in a heterogeneous cloud computing environment. The proposed HGA has different phases chief amongst them is to apply new crossover and mutation operators for global search, and a Walking around procedure to enhance the quality of local search solutions. It makes a good balance between local and global searches in a huge search space that leads to efficient results. To verify the proposed hybrid algorithm, it has been tested in different twelve scenarios with variable communication to computation ratios datasets. The results of extensive simulations in twelve datasets scenarios prove that HGA significantly dominates other state-of-the-art in terms of three prominent cost metrics, namely, makespan, monetary cost, and failure cost in the amount of 14.10%, 18.70%, and 42.30% cost reduction respectively.

关键词： task scheduling problem Cost-efficient Hybrid genetic algorithm Walking around procedure

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Towards optimal static task scheduling for realistic machine models: Theory and practice

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2003年第2期17卷 173-189页

作者： Boeres, C Rebello, VEF Univ Fed Fluminense Inst Comp Niteroi RJ Brazil

task scheduling is a key element in achieving high performance from multicomputer systems. Efficient scheduling algorithms reduce the interprocessor communication and improve processor utilization. To do so effectively, such algorithms must be based on a communication cost model appropriate for computing systems in use. The optimal scheduling of tasks is NP-hard, and a large number of heuristic algorithms have been proposed for a range of differing scheduling conditions (graph types, granularities and cost or architectural models). Unfortunately, due both to the variety of systems available and the rate at which these systems evolve, an appropriate representative cost model has yet to be established. In this paper we study the problem of task scheduling under a LogP-type model and we present both theoretical and experimental results for a cluster-based, task duplication methodology. The model is not only representative of parallel systems but also incorporates the traditional model used by the scheduling community. Results show that, for this model, an algorithm based on this LogP scheduling methodology can generate good makespans.

关键词： task scheduling problem clustering algorithms LogP model delay model

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Graph Coloring Inspired Approximate Algorithm for Wireless Energy Redistribution in WSNs

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2020年第1期4卷 124-138页

作者： Gao, Zhenguo Chen, Danjie Wu, Hsiao-Chun Huaqiao Univ Dept Comp Sci & Technol Quanzhou 362021 Peoples R China Huaqiao Univ Fujian Prov Key Lab Comp Vis & Machine Learning Xiamen 361021 Peoples R China Huaqiao Univ Dept Civil Engn Xiamen 361021 Peoples R China Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

As a new topic attracting research efforts recently, the Wireless Power Transfer based Energy ReDistribution (WPTERD) problem is targeted for finding energy transmission schedules satisfying the nodes' energy expectations, meanwhile achieving minimum energy loss and shortest time span. In this paper, we propose a two-step approach for WPTERD by dividing it into two sub-problems named WPTERD-Egy and WPTERD-Time, which focus on the optimization in energy loss and in time span respectively. Based on some proved properties, the WPTERD-Egy problem is transformed to a Linear Programming problem and thus an optimal time-length list of the nodes' energy transmissions achieving minimum energy loss can be obtain easily. For that WPTERD-Time is NP-hard, we propose an approximate algorithm by exploiting the core idea of the well-known least-degree-last graph coloring algorithm, and prove its approximation ratios respectively for 2D and 3D WSNs. Combining the two steps, we construct our Graph Coloring inspired Energy-Time Decoupling (GCEgyTimeD) algorithm for WPTERD. Numerical simulations validate GCEgyTimeD's ability in returning a schedule with minimum energy loss and with time span within 20% of the optimal value. Simulation results also show that GCEgyTimeD outperforms a representative flow-based algorithm in terms of the tested performance metrics.

关键词： Energy redistribution wireless power transfer/charging task scheduling problem graph coloring problem

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task scheduling for multi-tier shuttle warehousing systems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第19期53卷 5884-5895页

作者： Wang, Yanyan Mou, Shandong Wu, Yaohua Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China

Multi-tier shuttle warehousing systems are increasingly popular because of their high flexibility and robustness. These systems consist of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts undertakes inbound/outbound tasks instead of the stacker crane in conventional automated storage and retrieval systems. scheduling different devices to reduce expected cycle time is an important concern. Thus, we propose a time sequence mathematical model of task operation on the basis of the movement characteristics of shuttles and lifts. The task scheduling problem between shuttles and lifts is converted into an assembly line parallel job problem by analysing the mathematical model, which generates the scheduling task queue model in the specified time window. In addition, a Pareto optimal based on an elitist non-dominated sorting genetic algorithm is adopted to solve the multi-objective optimisation function in the task scheduling problem. Finally, we illustrate the findings of the study through a practical example. Results show that the optimisation scheduling solution can reduce the total lift idle time and the total shuttle waiting time. Furthermore, it can improve warehousing efficiency and lower operation costs.

关键词： task scheduling problem multi-tier shuttle warehousing system elitist non-dominated sorting genetic algorithm warehouse operation process

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Energy Loss Minimization for Wireless Power Transfer Based Energy Redistribution in WSNs

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第12期68卷 12271-12285页

作者： Gao, Zhenguo Chen, Danjie Wu, Hsiao-Chun Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 FJ Peoples R China Fujian Prov Univ Huaqiao Univ Key Lab Comp Vis & Machine Learning Xiamen 361021 FJ Peoples R China Huaqiao Univ Dept Civil Engn Xiamen 361021 Peoples R China Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

By enabling energy redistribution among nodes in Wireless Sensor Networks (WSNs), the Wireless Power Transfer (WPT) technology provides a new approach to prolong life time of WSNs or a fundamental block for building perceptual WSNs. In this paper, we attempt to deal with the underlying WPT based Energy ReDistribution (WPTERD) problem, which is targeted for finding an energy transmission schedule that satisfies the nodes' energy expectations, meanwhile achieves minimum energy loss and shortest time span. In this paper, we propose a two-step approach for WPTERD by splitting it into two sub-problems named WPTERD-Egy and WPTERD-Time, which focus on the optimization in energy loss and in time span respectively. Basing on some proved properties, we transform WPTERD-Egy to a Linear Programming problem and thus can obtain an optimal time-length list of the nodes' energy transmissions leading to minimum energy loss. Finding that WPTERD-Time is NP-hard, we propose an approximate algorithm to solve the WPTERD problem by exploiting the concurrent energy transmission opportunities. The algorithm is named as Greedy Energy ReDistribution scheduling (GERDS) algorithm, which is proved to finish in limited loops. Simulations validate the algorithm's ability in minimizing energy loss, and meanwhile return near-optimal schedules with time span in range 100 similar to 125% of the optimal value. Simulation results also show that GERDS outperforms a typical flow-based algorithm in terms of the tested performance metrics.

关键词： Wireless energy redistribution wireless power transfer/charging task scheduling problem

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Adjustable rotation gate based quantum evolutionary algorithm for energy optimisation in cloud computing systems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2024年第4期27卷 414-433页

作者： Chauhan, Jyoti Alam, Taj Jaypee Inst Informat Technol Dept Comp Sci & Engn & Informat Technol Noida 201309 UP India

The widespread adoption of cloud computing and a rapid rise in capacity and scale of data centres results in a significant increase in electricity usage, rising data centre ownership costs, and increased carbon footprints. One of the challenging research problems of this point in time is to reduce the energy consumption in cloud data centres which leads motivation of green cloud computing. This article presents an energy efficient improved quantum evolutionary algorithm (QEA) to provide optimal solution for NP-hard task scheduling problems in cloud computing. An adjustable quantum rotation gate operator with adaptively dynamic adjustment of the rotation angle is developed according to the evolutionary generations and change in fitness values in each generation which serves as a major operation to update the population of the conventional QEA. The efficiency and accuracy of proposed QEA are greatly improved to measure the energy efficiency, makespan and resource utilisation in heterogeneous CC environment. Simulation results of the CloudSim shows that proposed QEA is superior to traditional PSO and GA to solve NP-hard task scheduling problems in green CC.

关键词： cloud computing scheduling quantum evolutionary algorithm QEA rotation gate particle swarm optimisation PSO genetic algorithm energy efficiency task scheduling problem resource allocation makespan

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