Apache Spark Implementation of Whale Optimization Algorithm

AlJame, Maryam; Ahmad, Imtiaz; Alfailakawi, Mohammad Gh.

doi:10.1007/s10586-020-03162-7

Cited by 16 publications

(8 citation statements)

References 39 publications

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“…ZP et al [118] proposed the Levy flight strategy and ranking-based mutation operator to realize complementary benefits and thereby balance exploration and exploitation. Al Jame et al [119] used the Apache Spark platform for the distributed implementation of the WOA, to enhance its performance and reduce computational complexity. RY et al [120] set key parameters and classified search whales into different categories, to provide a faster local convergence rate, higher convergence accuracy, and lower computational complexity.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

View full text Add to dashboard Cite

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“…The second part of the experiment compared the average running time of the proposed parallel AOA algorithm to the Spark-based Whale Optimization Algorithm (Spark-WOA). The Spark-WOA (AlJame et al, 2020) implementation was tested on an EMR Yarn cluster comprising ten Amazon EC2 instances, including one master node and nine worker nodes. These instances were of the general purpose m4.large type, each with 2 vCPUs and 8 GiB of memory.…”

Section: Spark-aoa Evaluationmentioning

confidence: 99%

“…Therefore, many traditional metaheuristics such as genetic algorithm (Lu et al, 2020), particle swarm optimization (Al-Sawwa & Ludwig, 2020), differential evolution (He et al, 2021), whale optimization (AlJame et al, 2020), sine cosine (Alfailakawi et al, 2021), teaching-learning-based optimization (Wan et al, 2021), and grey wolf optimizer (Jarray et al, 2022a) have been successfully parallelized on Spark environments showing considerable performance gains for large scale problems. However, AOA being a recently proposed metaheuristic has not been parallelized under such an environment yet.…”

Section: Introductionmentioning

confidence: 99%

A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

AlJame

Al-Noori

Alfailakawi

et al. 2023

International Journal of Applied Metaheuristic Computing

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Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality.

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“…The WOA can solve a complex problem easily, but the problem is that it requires a huge amount of computation. Maryam AlJame and his co-workers proposed spark-WOA, which will reduce computational complexity and improve performance [4].…”

Section: Related Workmentioning

confidence: 99%

December 2020

2020

JSCP

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Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

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Apache Spark Implementation of Whale Optimization Algorithm

Cited by 16 publications

References 39 publications

A survey on several new popular swarm intelligence optimization algorithms

A survey on several new popular swarm intelligence optimization algorithms

A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

December 2020

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