A Multi-Objective Optimization via Simulation Framework for Restructuring Traffic Networks Subject to Increases in Population

Baquela, Enrique Gabriel; Olivera, Ana Carolina

doi:10.1007/978-3-319-58253-5_13

Cited by 2 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MOP methods have been applied to different applications, such as restructuring traffic networks [36], data mining methods for knowledge discovery [37], multiple sequence alignment [38], wind turbine blade geometry design [39] and others [40,41].…”

Section: Related Workmentioning

confidence: 99%

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Alresheedi

Elaziz

et al. 2019

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOS-SASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the

show abstract