Brain Storm Optimization Graph Theory (BSOGT) and Energy Resource Aware Virtual Network Mapping (ERVNM) for Medical Image System in Cloud

Palanikkumar, D.; Priya, Shivam

doi:10.1007/s10916-018-1155-7

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…The method includes the total optimization of energy costs, CO 2 emission minimization and electric power. The work in [24] applies the brain storm optimization graph theory into the medical image field for specifying energy computation mapping. Hao et al [25] develop a hybrid brain storm optimization approach to solve distributed hybrid flowshop scheduling problems.…”

Section: A Brain Storm Optimizationmentioning

confidence: 99%

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Peng

Wang

2020

IEEE Access

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Service composition provides an effective means to fulfill users' personalized requirements and complex business applications. With the number of web services rapidly growing, finding the best combination among services based on quality of service (QoS) poses a critical computational challenge due to the exponential growth of alternative composite services. Some efforts are developed to find the near-optimal service combination within an acceptable time range. They fall into two categories of solutions: exploring partial combinations in the service space and downsizing the optimization problem in scale. Although they solve the scalability problem to some extent, the required computational time is usually high. A promising direction is to integrate these two categories of solutions. However, its practical application suffers from three challenges: no good search scheme, no consideration of the natural organization of sub-problems, and the lack of diverse combinations. In this work, we propose a novel approach, called multi-clusters adaptive brain storm optimization (MCaBSO) algorithm. The proposed method uses brain storm optimization (BSO) as the search scheme to combine the division of search space with the exploration of the reduced search space. MCaBSO uses the twin support vector machine (TWSVM) to effectively divide the search space according to the natural organization of sub-problems. MCaBSO provides an adaptive dual strategy that gives guidance for the generation of diverse combinations. MCaBSO enables the agile exploration of the reduced search space and generates more high-quality combinations. MCaBSO is evaluated on two datasets to show effectiveness and efficiency.

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Section: A Brain Storm Optimizationmentioning

confidence: 99%

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Peng

Wang

2020

IEEE Access

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“…Compared with other intelligent optimization algorithms, BSO has the advantages of a compact mathematical model , simple operation, clear process, fast convergence speed and high optimization efficiency. Therefore, it is considered to be a very promising method, and it has been favored by many researchers and widely applied in practical optimization problems in different fields such as power systems [9], [10], [11], [12], [13], machine learning [14], [15], [16], [17], [18], [19], combinatorial optimization problems [20], [21], [22] and image processing [23], [24], [25] and prediction [15], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

“…A large number of scholars have paid increasing attention to BSO and have conducted in-depth research on it. According to the algorithm mechanism and application background, the research on the BSO algorithm can be divided into the following categories: (1) improving the clustering method of BSO [28], [29], [30], [31], [32], [33], [34], [35], [36]; (2) improving the new individual generation strategy [34], [37], [38], [39], [40], [41], [42], [43], [44]; (3) applying the research on BSO [9], [12], [15], [18], [38], [45], [46], [47]. The improvement and research of these algorithms from different directions not only improve the optimization performance of BSO but also promote the healthy development of BSO theory and applications.…”

Section: Introductionmentioning

confidence: 99%

“…In 2018, Xiong et al [12] proposed an improved BSO and applied it to solve fault section diagnosis (FSD) problems in power systems. In 2019, Palanikkumar and Priya [18] proposed an improved BSO and applied it to reliably manage medical information. In 2019, Hao et al [47] proposed a hybrid BSO and applied it to solve distributed hybrid flowshop scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

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An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

2020

IEEE Access

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The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this paper, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this paper consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.INDEX TERMS brain storm optimization algorithm, traveling salesman problem, hierarchical clustering, optimization algorithm, combinatorial optimization.

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Internet of Things and Cloud Convergence for eHealth Systems: Concepts, Opportunities, and Challenges

Ullah,

Aznaoui,

Sebai

et al. 2023

Wireless Pers Commun

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Brain Storm Optimization Graph Theory (BSOGT) and Energy Resource Aware Virtual Network Mapping (ERVNM) for Medical Image System in Cloud

Cited by 6 publications

References 17 publications

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

Internet of Things and Cloud Convergence for eHealth Systems: Concepts, Opportunities, and Challenges

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