A discrete cuckoo optimization algorithm for consolidation in cloud computing

Tavana, Madjid; Shahdi-Pashaki, S.; Teymourian, Ehsan; Santos‐Arteaga, Francisco J.; Komaki, Mohammad

doi:10.1016/j.cie.2017.12.001

Cited by 41 publications

(15 citation statements)

References 56 publications

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“…Just because most of the data sets have few numbers of features. Perhaps, if its demonstrated on high dimensional data or enhance to avoid redundancy among selected subsets, it may provide a better result as argued by [6].…”

Section: B Evolutionary Computation For Filter-based Feature Selectionmentioning

confidence: 99%

“…By nature, a cuckoo can only lay 5-20 eggs. In this study, the same concept was used that five cuckoos with less profit lay five eggs and also other fifteen cuckoos lay an egg in the interval [6,20] proportional to their profit. Thus, the total number of eggs will be 220.…”

Section: Cuckoo Optimisation Algorithmmentioning

confidence: 99%

“…However, most of them go through early convergence. Cuckoo optimisation algorithm (COA) is amongst the EC techniques that are testified in [6] to have resourceful exploration operatives that determine the whole promising area in the exploration space and converges earlier than many other EC based techniques. Based on that, binary COA (BCOA) is employ as a search method to explore the most appropriate subsets of feature automatically.…”

Section: Introductionmentioning

confidence: 99%

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Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

2020

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Feature selection aims to confiscate inappropriate features and yet improve classification performance. These aims are conflicting with one another, and a choice must be made in the presence of the trade-off between them. Numerous researches deal with feature selection problem but, they are mostly single-objective based. Nowadays, multi-objective optimisation approaches are becoming the most suitable approaches to deal with feature selection problems. They can easily create a balance between selected features and classification accuracy or error rate. Evolutionary computation techniques have been applied for multi-objective feature selection. Cuckoo optimisation algorithm is among the most popular technique that is exceptional in solving the problems of feature selection. Based on the binary cuckoo optimisation algorithm, two different multi-objective filter-based feature selection frameworks are presented with the idea of nondominated sorting genetic algorithms NSGAIII (BCNSG3) along with NSGAII (BCNSG2). Thus, four multi-objective filter-based feature selection approaches are proposed by employing mutual information along with gain ratio based-entropy as the respective filter evaluation measures in all the proposed frameworks. The results obtained are examined and analysed against the existing methods and single objective scheme on fourteen (14) datasets of varying degree of difficulties. The outcome of the experiments displays that the proposed multi-objective algorithms successfully derive a set of nondominated solutions that used the least feature size and attained the best error rate than using full-length features. In general, BCNSG2 obtained the best results compared to the existing methods and single-objective algorithm, whereas BCNSG3 outdoes all other approaches. INDEX TERMS Cuckoo optimization algorithm, multi-objective feature selection, NSGA III, NSGA II, gain ratio based-entropy, mutual information, machine learning and classification.

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Section: B Evolutionary Computation For Filter-based Feature Selectionmentioning

confidence: 99%

Section: Cuckoo Optimisation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

2020

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“…Many heuristic algorithms have been used in the VM placement problem: ant-colony-based VM placement is formulated as a multidimensional bin-packing problem to control resource wastage and energy consumption simultaneously [12]. Group technology-based cuckoo optimization technology is used to control the datacenter's operating cost, considering task migration, VM creation and energy consumption [13]. An improved practical swarm optimization is used to increase the quality of service with reduced power consumption [14].…”

Section: Related Workmentioning

confidence: 99%

“…The new harmony generated in step 2 is checked for feasibility using the constraints given in Equations (9)- (13). If the new harmony found is better than the worst harmony in the HMV, the worst harmony is updated.…”

Section: The Measure Of Fitness and Hmv Updatementioning

confidence: 99%

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

et al. 2020

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Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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Binary Cuckoo Optimisation Algorithm and Information Theory for Filter-Based Feature Selection

Usman

Yusof

Naim

2021

Lecture Notes on Data Engineering and Communications Technologies

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A discrete cuckoo optimization algorithm for consolidation in cloud computing

Cited by 41 publications

References 56 publications

Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

Binary Cuckoo Optimisation Algorithm and Information Theory for Filter-Based Feature Selection

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