A Multi-objective Optimization Framework for Multiple Sequence Alignment with Metaheuristics

Zambrano-Vega, Cristian; Nebro, Antonio J.; García-Nieto, José; Aldana‐Montes, José F.

doi:10.1007/978-3-319-56154-7_23

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…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.

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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

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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.

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“…Diverse approaches to software libraries for implementing heuristic algorithms can be found in the literature. Some of them are targeted to specific domains, such as urban economic modeling, vehicle routing problems, multiple sequence alignment, hydrologic model calibration, and project scheduling . Other libraries cover different search methods and implementation issues, from a broader scope of heuristic algorithms, nature‐inspired metaheuristics, memetic algorithms, local search metaheuristics, and visualization of genetic algorithm components …”

Section: Software Design Support For Heuristic Algorithmsmentioning

confidence: 99%

hMod: A software framework for assembling highly detailed heuristics algorithms

et al. 2019

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Summary Software design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod.

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Flower Pollination Algorithm With Profile Technique For Multiple Sequence Alignment

Hussein

Abdullah

AbdulRashid

2019

2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

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A Multi-objective Optimization Framework for Multiple Sequence Alignment with Metaheuristics

Cited by 5 publications

References 25 publications

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

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

hMod: A software framework for assembling highly detailed heuristics algorithms

Flower Pollination Algorithm With Profile Technique For Multiple Sequence Alignment

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