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A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm
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Author(s): Maryam AlJame (Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait), Aisha Alnoori (Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait), Mohammad G. Alfailakawi (Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait)and Imtiaz Ahmad (Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait)
Copyright: 2023
Volume: 14
Issue: 1
Pages: 27
Source title:
International Journal of Applied Metaheuristic Computing (IJAMC)
Editor(s)-in-Chief: Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/IJAMC.318642
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Abstract
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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