scholarly journals Hybrid Algorithm using the Advantage of Krill Herd Algorithm with Opposition- Based Learning for Dynamic Resource Allocation in Cloud Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 306-311
Keyword(s):  
Resource Allocation ◽  
Optimization Algorithm ◽  
Computational Cost ◽  
Dynamic Resource Allocation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Opposition Based Learning ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Dynamic Resource

The cloud computing systems have more consideration due to the growing control for elevated concert computing and data storage. Resource allocation plays a vital role in cloud systems. To overcome the obscurity present in resources allocation system. In this paper, we design and develop a technique for dynamic resource allocation. A Hybridized approach is designed with the help of multi-objective oppositional krill herd optimization algorithm (OKHA). It is a combination of the krill herd algorithm and Opposition-based learning (OBL), OBL is added to get enhanced performance of the krill herd algorithm. The objective of this hybridization is to reduce the cost. In this Hybridized process each task consists of two cost i.e monetary cost and computational cost. Here each task is divided into many subtasks and assigns the respective resources to it. Our proposed multi-objective optimization algorithm will decide allocation of resource for the each subtask in this process. Finally, the testing is passed out, we evaluate our proposed algorithm with PSO, and GA algorithm we verified the performance levels of our proposed Multi-objective optimization algorithm.

scholarly journals A Free Search Krill Herd Algorithm for Functions Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Liangliang Li ◽  
Yongquan Zhou ◽  
Jian Xie
Keyword(s):  
Optimization Algorithm ◽  
Search Strategy ◽  
Optimal Solution ◽  
High Dimensional ◽  
Opposition Based Learning ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Local Optimal Solution ◽  
Free Search ◽  
Low Dimensional

To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.

scholarly journals MOEA/D com Busca Local para o Flow Shop Multiobjetivo

2019 ◽  
Author(s):  
Geovani Antunes ◽  
Carolina Almeida ◽  
Ricardo Lüders ◽  
Sandra Venske ◽  
Myriam Delgado
Keyword(s):  
Resource Allocation ◽  
Evolutionary Algorithm ◽  
Flow Shop ◽  
Dynamic Resource Allocation ◽  
Total Tardiness ◽  
Multi Objective ◽  
Dynamic Resource

Este artigo aborda o Flow Shop de Permutação, um problema de sequenciamento presente em muitos mecanismos de gerenciamento de processos de produção industrial. A abordagem multiobjetivo considerada neste trabalho envolve a minimização do tempo máximo para completar um trabalho (makespan) e do tempo total de atraso (total tardiness). Para isso é utilizada uma plataforma multiobjetivo denominada MOEA/D-DRA (do inglês Multi-objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation). O foco do trabalho reside na utilização de um mecanismo muito conhecido por seus bons resultados nas versões mono-objetivo do problema. Este mecanismo, denominado NEH, é adaptado para ser utilizado na busca local incluída no MOEA/D-DRA, aplicado na solução de 11 instâncias do Flow Shop de permutação com tamanhos variando de 20 a 200 tarefas e 5 a 20 máquinas. A abordagem proposta é comparada com o MOEA/D-DRA utilizando NEH apenas na inicialização da população. Os resultados mostram que, em apenas 2 instâncias, a versão do MOEA/D-DRA sem busca local supera a abordagem proposta.

scholarly journals A traffic-awareness dynamic resource allocation scheme based on multi-objective optimization in multi-beam mobile satellite communication systems

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772355 ◽  
Author(s):  
YuanZhi He ◽  
YiZhen Jia ◽  
XuDong Zhong
Keyword(s):  
Resource Allocation ◽  
Communication Systems ◽  
Satellite Communication ◽  
Dynamic Resource Allocation ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Multiple Beams ◽  
Traffic Awareness ◽  
Dynamic Resource ◽  
Mobile Satellite

Mobile satellite communication systems play an important role in space information networks. They mostly operate at the L or S band and have multiple beams efficiently reusing the limited spectrum. Advanced technologies, such as beamforming, are used to generate numerous beams through multiple feeders, and each beam’s power allocation is correlated and constrained. Frequency reuse among multiple beams results in co-channel interference issue, which makes bandwidth allocation among multiple beams coupled. It is a challenging topic to optimize the resource allocation in the real-time service traffic. In this article, a new multi-objective programming scheme is used to solve the dynamic resource allocation problem, guaranteeing high quality-of-service for multiple services of different priorities. Since the dynamic resource allocation problem is formulated as NP-hard, a new traffic-aware dynamic resource allocation (TADRA) algorithm is proposed. This algorithm is proved to be optimal in terms of the Pareto-front under constraints of co-channel interference and onboard transmit power. Simulation results show that the trade-off is well balanced between the call completion ratio in high priority and the throughput for video and data services in medium and low priorities. Additionally, it is shown that the new multi-objective programming scheme, based on the traffic-awareness dynamic resource allocation algorithm, can rapidly achieve the Pareto-front solutions and reduce the computing complexity to a large extent.

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