Spark‐based parallel processing whale optimization algorithm

Abstract

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.

Whale Optimization Algorithm-Based Parallel Computing for Accelerating Misalignment Estimation of Reflective Fourier Ptychography Microscopy

“…Sauber et al first introduced a parallel whale optimization algorithm based on the OpenMP library of the SMP approach in 2018 [29]. Afterwards, Alsayeji et al prompted the Apache spark-based parallel processing WOA (SBWOA) to overcome the disk overheads by using resilient distributed datasets and showed higher computing performance [24].…”

Reducing the time needed to solve a traveling salesman problem by clustering with a Hierarchy-based algorithm

“…These algorithms need comprehensive tests from the benchmark functions to evaluate their performance. The WOA algorithm has shown good results compared with other meta-heuristics so it is being used in different fields of engineering such as [3]: optimizing the placement of capacitors, Making feature selection techniques, solving the economic dispatch problem, enhancing the performance of photovoltaic power systems, efficiently balancing energy production with the load demand [3], finding proper coefficients, cost minimization, and feature selection based on whale optimization algorithm (FSWOA) [4] with the aim to reduce the dimensionality of medical data [5] by the selection of a reduced feature set.…”