An Effective Hybrid Algorithm Based on Particle Swarm Optimization with Migration Method for Solving the Multiskill Resource-Constrained Project Scheduling Problem

Quoc, Huu Dang; Lộc, Nguyễn Thế; Doan, Cuong Nguyen

doi:10.1155/2022/6230145

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…The best particle is the best result (expressing the value of the fitness function) that has been achieved so far. The objective function (F) is determined by the best position for each p, along with the p with the best position in the current set (Liu et al, 2016;Bharath et al, 2022;Moh et al, 2022;Mercangöz, 2021;Quoc et al, 2022;Ali and Tawhid, 2017;Saber et al, 2021).…”

Section: Preliminaries and Methodsmentioning

confidence: 99%

An Efficient Cloud Computing System for E-Health

Omar,

El Hadidi,

El-harby

et al. 2023

Scientific Journal for Damietta Faculty of Science

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Cloud computing has become an indispensable component of the E-health services sector. Based upon, the data of E-Health cloud is increasing day by day as a result of increasing patient data and medical team diagnoses. This leads to a huge amount of data being stored that is easy to lose during disaster occurred. In order to that, an optimization model named The Particle Swarm Multi-Objective Optimization (PSMOO) was proposed for serving and recovering data of the E-Health cloud during system failure. The Particle Swarm Optimization (PSO) algorithm was used for the E-Health data scheduling procedure. The proposed model has the ability to play an important role in achieving the reliability of the cloud computing E-Health environment by considering the available resources to prevent data loss. The model compared PSO and genetic algorithms to show that the proposed model is robust, integrated and reliable for the disaster recovery.

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Section: Preliminaries and Methodsmentioning

confidence: 99%

An Efficient Cloud Computing System for E-Health

Omar,

El Hadidi,

El-harby

et al. 2023

Scientific Journal for Damietta Faculty of Science

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“…These EA algorithms contribute to the early-stage development of an optimal schedule, benefiting project management endeavors. Dang et al [27] present M-PSO, a novel particle swarm optimization technique. To enhance exploration, the algorithm incorporates a migration method to escape local optima and broaden the search space.…”

Section: Related Workmentioning

confidence: 99%

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

Akbar,

Zubair,

Khan

et al. 2024

IEEE Access

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This study addresses the Weighted Multi-Skill Resource Constrained Project Scheduling Problem (W-MSRCSPSP) with the aim of minimizing software project makespan. Unlike previous works, our investigation regards heterogeneous resources characterized by varying skill proficiency levels. Another major problem with existing methodologies is the potential underutilization of human resources due to varying task durations. This work introduces an innovative scheduling approach known as the Greedy and Parallel Scheduling (GPS) algorithm to handle the said issues. GPS focuses on assigning the most suitable resources available to project activities at each scheduling point. The fundamental goal of our proposed approach is to reduce resource wastage while efficiently allocating surplus resources, if any, to project tasks, ultimately leading to a decrease in the makespan. To empirically evaluate the efficacy of the GPS algorithm, we conduct a comparative analysis against the Parallel Scheduling Scheme (PSS). The advantage of our proposed approach lies in its ability to optimize the utilization of available resources, resulting in accelerated project completion. Results from extensive simulations substantiate this claim, demonstrating that the GPS scheme outperforms the PSS approach in minimizing project duration.

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“…Reference 20 suggests a new algorithm to overcome the drawback of sliding into local extremes by combining the Migration method with the standard PSO. The suggested strategy finds local extremes in population evolution and suggests a fresh way to avoid them by migrating the population to a new values space.…”

Section: Related Studiesmentioning

confidence: 99%

A chaos opposition‐based dwarf mongoose approach for workflow scheduling in cloud

Talha

Bouayad

Malki

2023

Trans Emerging Tel Tech

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Cloud computing (CC) systems are a form of public infrastructure that has been in use since its beginnings. In such technologies, clients can access current services according to their requirements without knowing where the service is hosted or how it is supplied, and only pay for the services that they really consume. The CC system faces several challenges. Due to the large range of clients and services offered by this platform, it can be concluded that the scheduling problem and resource usage are its primary issues. In order to successfully balance the three main qualities of service (QoS) criteria: time, cost, and resource usage, researchers were inspired to improve the workflow scheduling (WFS) methods in cloud. Since the randomness of the initial population and the inadequate exploration/exploitation capacity, which has an impact on the quality of the solutions, traditional intelligent optimization algorithms have a poor convergence rate when it comes to resource scheduling. In this research, we suggested a hybridization method combining the Dwarf Mongoose algorithm (DWO) and Chaos opposition based learning algorithm called CO‐DWO in an attempt to develop a more effective optimization algorithm for workflow scheduling issue. The CO‐DWO can be much more advantageous in optimization with a better convergence rate in comparison with other literature techniques.

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An Effective Hybrid Algorithm Based on Particle Swarm Optimization with Migration Method for Solving the Multiskill Resource-Constrained Project Scheduling Problem

Cited by 5 publications

References 27 publications

An Efficient Cloud Computing System for E-Health

An Efficient Cloud Computing System for E-Health

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

A chaos opposition‐based dwarf mongoose approach for workflow scheduling in cloud

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