Article
Automation & Control Systems
B. Mohammad Hasani Zade, N. Mansouri, M. M. Javidi
Summary: This study introduces a hybrid metaheuristic algorithm called HFHB for task scheduling problems, which combines fuzzy features and optimization algorithms to achieve significant progress in solving multi-objective problems. The algorithm demonstrates better performance compared to other algorithms in experimental evaluations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Behnam Mohammad Hasani Zade, Najme Mansouri, Mohammad Masoud Javidi
Summary: The study developed a fuzzy-based task scheduling (SAEA) algorithm to address the difficult task scheduling problem, incorporating energy cost, makespan, degree of imbalance, and security levels for multi-objective optimization. The experiment results showed that SAEA algorithm outperforms other similar scheduling algorithms in terms of energy cost reduction and overall performance under high load conditions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Analytical
Said Nabi, Masroor Ahmad, Muhammad Ibrahim, Habib Hamam
Summary: This paper presents an adaptive task scheduling approach based on Particle Swarm Optimization (PSO), which improves task execution time, throughput, and average resource utilization ratio (ARUR). It introduces an adaptive inertia weight strategy called Linearly Descending and Adaptive Inertia Weight (LDAIW) to achieve a better balance between local and global search. The proposed approach is compared with renowned PSO-based inertia weight strategies and other well-known meta-heuristic scheduling approaches, and the results show significant improvements in makespan, throughput, and ARUR.
Article
Chemistry, Multidisciplinary
Nimra Malik, Muhammad Sardaraz, Muhammad Tahir, Babar Shah, Gohar Ali, Fernando Moreira
Summary: This article addresses the issue of energy consumption and efficient resource utilization in virtualized cloud data centers, proposing an algorithm based on task classification and thresholds for efficient scheduling. Experiments validate the effectiveness of the proposed technique over other algorithms in terms of energy consumption, makespan, and load balancing.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Xueliang Fu, Yang Sun, Haifang Wang, Honghui Li
Summary: This paper proposes a cloud task scheduling method based on particle swarm optimization genetic hybrid algorithm. By using phagocytosis mechanism and crossover mutation of genetic algorithm to change the position of particles, the search range of the solution space is expanded. Experimental results show that the proposed algorithm significantly improves the completion time of cloud tasks and has high convergence accuracy, demonstrating the effectiveness of the algorithm in cloud task scheduling.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Kaushik Mishra, Jharashree Pati, Santosh Kumar Majhi
Summary: This article discusses the importance of handling dynamic workloads in datacenters and the challenges that can lead to server imbalance. To address the fluctuating resource provisioning needs, a method based on binary JAYA is proposed for load scheduling and load balancing, aiming to improve resource utilization and reduce energy consumption and makespan.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Chirag Chandrashekar, Pradeep Krishnadoss, Vijayakumar Kedalu Poornachary, Balasundaram Ananthakrishnan, Kumar Rangasamy
Summary: With the advancement of technology and time, various algorithms have been proposed to improve the performance of individual units or structures used in the cloud environment. Task scheduling is one of the most important sections of cloud computing, responsible for optimizing the time taken to execute processes and improving efficiency. This paper proposes an ideal and optimal task scheduling algorithm and compares it with other existing algorithms in terms of efficiency, makespan, and cost parameters.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Sudheer Mangalampalli, Ganesh Reddy Karri, Utku Kose
Summary: Task scheduling is a significant challenge in cloud computing, and an efficient scheduling mechanism is needed to dynamically allocate resources based on user requests. Ineffective scheduling can lead to increased makespan, energy consumption, and violation of SLAs, resulting in degraded service quality and trust. In this study, a multi-objective trust-aware scheduler was designed, using the Whale Optimization algorithm to schedule tasks to virtual resources while minimizing makespan and energy consumption. Simulation results showed a significant improvement in makespan, energy consumption, total running time, and trust parameters.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
B. Gomathi, S. T. Suganthi, Karthikeyan Krishnasamy, J. Bhuvana
Summary: The paper applies the MOIMBO algorithm to solve multi-objective task scheduling problems in the cloud, considering three different dispute objectives and using the Epsilon fuzzy dominance sort method to select the best solutions. The algorithm combines self-adaptive and greedy strategies, showing superior performance and excellent results in terms of makespan, reliability, and resource utilization.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Information Systems
R. Ghafari, N. Mansouri
Summary: This paper proposes a multi-objective task scheduling algorithm DCOHHOTS based on a modified Harris hawks optimizer, aiming to optimize resource utilization and reduce makespan, energy consumption, and execution cost. The algorithm prioritizes tasks using a hierarchical process, and experimental results show that it can save up to 16% energy and increase resource utilization by 17% in heavy loads compared to existing algorithms. Furthermore, the proposed algorithm reduces makespan and execution cost by 26% and 8% respectively, compared to the conventional algorithm.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xingwang Huang, Yangbin Lin, Zongliang Zhang, Xiaoxi Guo, Shubin Su
Summary: This paper proposes a task scheduling method based on gradient-based optimization, which converts real vector values to integer values to solve the task scheduling problem in cloud computing systems. Experimental results show that this method has better convergence speed and accuracy in searching for optimal solutions compared to current heuristic algorithms, especially in the presence of large-scale tasks.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Muhammad Usman Sana, Zhanli Li, Fawad Javaid, Muhammad Wahab Hanif, Imran Ashraf
Summary: This study proposes a novel encoding technique using blockchain and Improved Particle Swarm Optimization (IPSO) to improve the makespan value and scheduling time. The experimental results indicate that the proposed algorithm is practical and secure in handling flexible job scheduling and outperforms the state-of-the-art task scheduling algorithms.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Arabinda Pradhan, Sukant Kishoro Bisoy
Summary: This paper proposes a load balancing technique using modified PSO task scheduling to balance tasks in a cloud computing environment, aiming to minimize the makespan and maximize resource utilization.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Said Nabi, Masroor Ahmed
Summary: This paper proposes an improved PSO-RDAL algorithm to enhance the efficiency and performance of task scheduling in cloud computing. Experimental results show significant improvements in various aspects compared to existing task scheduling heuristics.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Gaurav Agarwal, Sachi Gupta, Rakesh Ahuja, Atul Kumar Rai
Summary: Multiprocessor task scheduling is the operation of processing more than two tasks simultaneously in the system. The fog-cloud multiprocessor computing structures are a category of exchanged collateral structures with high demand. However, the existing fog-cloud system faces challenges such as scheduling and energy consumption due to excess clients and various services. To overcome these challenges, a hybrid genetic algorithm and energy conscious scheduling approach is proposed, which integrates genetic algorithm and energy conscious scheduling model. The proposed method has been compared with existing methods and proven to be more efficient.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad Masoud Javidi
Summary: The study introduces a multi-objective feature selection method, GBMA, based on BMA and Nash equilibrium approach to maximize model accuracy and minimize feature numbers through a simplified procedure. GBMA consists of four steps involving defining players, feature clustering, feature weighting, and player updating. The strategy explores the search space efficiently and finds optimal solutions without exhaustively examining all possibilities.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Najme Mansouri, Mohammad Masoud Javidi, Behnam Mohammad Hasani Zade
Summary: The cloud computing environment is becoming more interesting with data replication being widely applied. A proposed dynamic replication algorithm, HDRS, effectively manages replicas based on actual needs to reduce response time and bandwidth usage. It can identify popular files and replicate them to optimal sites, avoiding useless replications and improving load balancing.
FRONTIERS OF COMPUTER SCIENCE
(2021)
Article
Automation & Control Systems
B. Mohammad Hasani Zade, N. Mansouri, M. M. Javidi
Summary: This study introduces a hybrid metaheuristic algorithm called HFHB for task scheduling problems, which combines fuzzy features and optimization algorithms to achieve significant progress in solving multi-objective problems. The algorithm demonstrates better performance compared to other algorithms in experimental evaluations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Ali Mahmoudabadi, Marjan Kuchaki Rafsanjani, Mohammad Masoud Javidi
Summary: This paper introduces an online fuzzy approach for clustering data streams based on the growing neural gas algorithm, with more restrictive criteria for selecting winner nodes in the topological graph, showing improvements over existing clustering methods when tested on public datasets.
Article
Computer Science, Artificial Intelligence
Sepehr Ebrahimi Mood, Ming Ding, Zihuai Lin, Mohammad Masoud Javidi
Summary: This paper presents a novel method to optimize the application of UAVs as aerial base stations in IoT systems. By calculating UAV trajectory, device-to-UAV association, and transmission power, the link quality and energy consumption are optimized using a constrained gravitational search algorithm. Through simulations, it is demonstrated that the proposed optimization algorithm can increase system throughput and reduce energy consumption in UAV-based IoT systems.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Fatemeh Aghaeipoor, Mohammad Masoud Javidi, Alberto Fernandez
Summary: This article introduces an interpretable fuzzy classifier for Big Data, aiming to boost explainability by learning a compact yet accurate fuzzy model. Developed in a cell-based distributed framework, IFC-BD goes through three working stages of initial rule learning, rule generalization, and heuristic rule selection to move from a high number of specific rules to fewer, more general and confident rules. The proposed algorithm was found to improve the explainability and predictive performance of fuzzy rule-based classifiers in comparison to state-of-the-art approaches.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Mohammad Hasani Zade, N. Mansouri, M. M. Javidi
Summary: Most studies on task scheduling in the cloud focus on a few objectives, but this paper designs a task scheduling problem with conflicting objectives. The proposed algorithm consists of a meta-scheduler and a local-scheduler, and it is evaluated through experiments, demonstrating its performance improvement in waiting time, energy consumption, resource utilization, and security.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Automation & Control Systems
Zahra Asghari Varzaneh, Soodeh Hossein, Sepehr Ebrahimi Mood, Mohammad Masoud Javidi
Summary: Feature selection is a crucial preprocessing step in data mining and machine learning, aiming to remove irrelevant features from the dataset to improve algorithm performance. This paper proposes a novel feature selection model that utilizes an improved equilibrium optimization algorithm to extract the best features. Experimental results demonstrate the effectiveness of the proposed model in solving feature selection problems.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Behnam Mohammad Hasani Zade, Najme Mansouri, Mohammad Masoud Javidi
Summary: This paper proposes a dynamic data replication algorithm based on an improved ant lion optimizer algorithm and a fuzzy system, which considers the trade-offs among objectives and overcomes the premature convergence issue of the ant lion optimizer algorithm.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Zahra Asghari Varzaneh, Soodeh Hosseini, Mohammad Masoud Javidi
Summary: Feature selection is important for improving the performance of classification by removing useless features from the data set in machine learning problems. This paper proposes an improved version of Horse herd Optimization Algorithm (HOA) called BHOA as a wrapper-based feature selection method. S-Shaped and V-Shaped transfer functions are considered to convert continuous search space to discrete search space. Furthermore, the Power Distance Sums Scaling approach is used to control selection pressure, exploration, and exploitation capabilities. The implementation results on 17 standard benchmark datasets demonstrate the efficiency of the proposed method based on the V-shaped transfer functions compared to other transfer functions and other wrapper-based feature selection algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Soghra Mousavi, Sepehr Ebrahimi Mood, Alireza Souri, Mohammad Masoud Javidi
Summary: In recent years, the development of Internet of Things (IoT) has made it one of the most important technologies. The fog computing architecture has partially addressed the issue of latency and other limitations of the IoT-based cloud computing paradigm, but an appropriate and efficient task scheduling method considering energy consumption is still needed. This article proposes a constraint bi-objective optimization problem and a directed non-dominated sorting genetic algorithm (D-NSGA-II) to minimize servers' energy consumption and overall response time simultaneously. Experimental results show that D-NSGA-II outperforms other algorithms and can meet all request deadlines.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Retraction
Multidisciplinary Sciences
Reza Jafari, Mohammad Masoud Javidi
SN APPLIED SCIENCES
(2023)
Article
Engineering, Multidisciplinary
A. Zandvakili, N. Mansouri, M. M. Javidi
Summary: Task scheduling is a fundamental issue in cloud computing, and the proposed scheduling algorithm based on the Discrete Pathfinder Algorithm (DPFA) shows significant improvements in performance compared to other algorithms. The algorithm considers multiple objectives and achieves better resource utilization, throughput, and energy consumption results.
INTERNATIONAL JOURNAL OF ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Hamed Tabrizchi, Mohammad Masoud Javidi, Vahid Amirzadeh
Summary: The increasing human population, building constructions, and technology usage have led to a significant growth in electricity consumption. Efficient energy management and predicting energy consumption for buildings are crucial for energy saving and development. Artificial intelligence and machine learning methods play a vital role in forecasting building energy consumption and efficiency.
Article
Computer Science, Hardware & Architecture
N. Mansouri, M. M. Javidi, B. Mohammad Hasani Zade
Summary: Cloud computing has a significant impact on information technology solutions for organizations and researchers, with data fragmentation and replication algorithms playing a crucial role in enhancing data security. The proposed CSO-based SDR method effectively balances objectives through an intelligent fuzzy inference system.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiaolin Wang, Liyi Zhan, Yong Zhang, Teng Fei, Ming-Lang Tseng
Summary: This study proposes an environmental cold chain logistics distribution center location model to reduce transportation costs and carbon emissions. It also introduces a hybrid arithmetic whale optimization algorithm to overcome the limitations of the conventional algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hong-yu Liu, Shou-feng Ji, Yuan-yuan Ji
Summary: This study proposes an architecture that utilizes Ethereum to investigate the production-inventory-delivery problem in Physical Internet (PI), and develops an iterative heuristic algorithm that outperforms other algorithms. However, due to gas prices and consumption, blockchain technology may not always be the optimal solution.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Paraskevi Th. Zacharia, Elias K. Xidias, Andreas C. Nearchou
Summary: This article discusses the assembly line balancing problem in production lines with collaborative robots. Collaborative robots have the potential to improve automation, productivity, accuracy, and flexibility in manufacturing. The article explores the use of a problem-specific metaheuristic to solve this complex problem under uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
