A hybrid multi‐objective firefly and simulated annealing based algorithm for big data classification

Abstract

Summary Efficient management of big data becomes challenging in recent decades. Online Feature Selection (OFS) is one type of online learning in contrast to batch learning, allowing a classifier to have small and fixed number of features. The major aim of this work is to introduce an OFS algorithm supported on meta‐heuristic algorithm that exploits the MapReduce paradigm. A novel Hybrid Multi‐Objective Firefly and Simulated Annealing (HMOFSA) algorithm is proposed to select optimal set of features. Therefore, as a first step, the original big dataset is decomposed into blocks of examples in the map phase. Subsequently, HMOFSA algorithm is employed to choose the selected features from examples. After that, the attained partial outcomes will be combined into a final vector of features in the reduce phase and evaluated using Kernel Support Vector Machine (KSVM) classifier. The mentioned OFS approach is analyzed with the help of the well‐known classifiers (Logistic Regression, KSVM and Naïve Bayes) developed within the Spark framework. Experiments were conducted on big datasets, containing 66 million samples and 2000 attributes that confirm the proficiency of proposed work. The proposed KSVM classifier results are measured in terms of the metrics like Precision, Recall, Geometric‐mean (G‐mean), F‐measure, and accuracy.

Multiobjective Simulated Annealing: Principles and Algorithm Variants

“…Due to its computational efficiency and simplicity, simulated annealing has been widely used to solve multiobjective optimisation problems, as well as mono-objective ones, in various fields including clustering [65][66][67], jobshop problems [68], and scheduling [69]. Besides, genetic algorithms and particle swarm optimisation methods have received much attention in the recent years, and many commercial solvers permit simple implementation of such methods.…”

A modified firefly algorithm based on neighborhood search

“…29 To implement big data clustering, FA and simulated annealing were used to construct a hybrid multi-objective algorithm. 30 There are several scheduling problems in cloud computing. Arunarani et al combined FA and bat algorithm (BA) to solve the workflow scheduling.…”

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

“…From a problem type perspective, service deployment problem is the bin packing problem. There are many existing method to solve the bin packing problem, such as the Simulated Annealing(SA) [28], [29], the genetic algorithm (GA) [30]- [34], particle swarm optimization (PSO) [23], [35], [36]. Among them, the simulated annealing algorithm has a strong local search ability.…”