A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

Maryam AlJame, Aisha Alnoori, Mohammad G. Alfailakawi and Imtiaz Ahmad
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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
Imtiaz Ahmad: Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait

International Journal of Applied Metaheuristic Computing (IJAMC), 2023, vol. 14, issue 1, 1-27

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.

Date: 2023
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