A parallel whale optimization algorithm and its implementation on FPGA

Jiang, Qiangqiang; Guo, Yuanjun; Yang, Zhile; Zhou, Xianyu

doi:10.1109/cec48606.2020.9185737

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…On the other hand, additional benchmark functions [43][44][45][46][47][48][49][50] are tested using SBWOA against serial implementation of WOA applied in a single node. Tables A5 and A6 outline unimodal and multimodal benchmark functions, respectively, which will be used in this test.…”

Section: Methodsmentioning

confidence: 99%

Spark‐based parallel processing whale optimization algorithm

Alshayeji

Behbehani

Ahmad

2021

Concurrency and Computation

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Swarm intelligence meta-heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta-heuristic optimization algorithms. However, complex applications might require the processing of large-scale computations, which results in down-scaling computational throughput of WOA. Apache Spark, a well-known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR-WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher-complexity problems which require complex computations.

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Section: Methodsmentioning

confidence: 99%

Spark‐based parallel processing whale optimization algorithm

Alshayeji

Behbehani

Ahmad

2021

Concurrency and Computation

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“…u(x, y) = 0, (x, y) ∈ 𝜕Ω, (33) where the coefficients are a(x, y) = 2 − x 2 − y 2 b(x, y) = e (x−y) .…”

Section: Example 3: Elliptic Equationmentioning

confidence: 99%

“…These algorithms have received popularity in last decade as due to lower run time some of the parallel algorithms can be used for the applications that demand the output within a specific interval of time like resource constrained job scheduling, 26 direction of arrival estimation using sensor array, 27 quick three-dimensional path planning. 28 Some prominent recently reported such algorithms are: parallel PSO, 29 parallel differential evolution, 30 parallel ant colony optimization, 26 parallel artificial bee colony algorithm, 31 parallel social spider optimization (PSSO), 32 parallel whale optimization algorithm, 33 parallel compact cuckoo search algorithm, 28 parallel chaotic sailfish optimization, 27 parallel fish migration optimization. 34 The key contribution of this article is as follows:…”

Section: Introductionmentioning

confidence: 99%

Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

Panda

2022

Concurrency and Computation

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“…For a straightforward parallelism, in each iteration, we can assign the computing tasks of each agent (lines 7-17 in Algorithm 1) to a core (or a machine). Moreover, advanced approaches with parallelism over GPU and FPGA [35], [36] as well as effective convergence strategies [37] are also available for metaheuristics. Although we can integrate these approaches in NEAL, the details will be outside the scope of this work.…”

Section: Overhead Of Implementationmentioning

confidence: 99%

Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers

Cheng

Wang

Liu

et al. 2021

IEEE Trans. Parallel Distrib. Syst.

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Large data centers are currently the mainstream infrastructures for big data processing. As one of the most fundamental tasks in these environments, the efficient execution of distributed data operators (e.g., join and aggregation) are still challenging current data systems, and one of the key performance issues is network communication time. State-of-the-art methods trying to improve that problem focus on either application-layer data locality optimization to reduce network traffic or on network-layer data flow optimization to increase bandwidth utilization. However, the techniques in the two layers are totally independent from each other, and performance gains from a joint optimization perspective have not yet been explored. In this paper, we propose a novel approach called NEAL (NEtwork-Aware Locality scheduling) to bridge this gap, and consequently to further reduce communication time for distributed big data operators. We present the detailed design and implementation of NEAL, and our experimental results demonstrate that NEAL always performs better than current approaches for different workloads and network bandwidth configurations.

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A parallel whale optimization algorithm and its implementation on FPGA

Cited by 8 publications

References 27 publications

Spark‐based parallel processing whale optimization algorithm

Spark‐based parallel processing whale optimization algorithm

Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers

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