Improved Moth-Flame Optimization Based on Opposition-Based Learning for Feature Selection

Mohamed Abd Elaziz, Songfeng Lu, Diego Oliva, Mohammed El-Abd

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an improvement for the Moth-flame Optimization (MFO) algorithm is proposed based on Opposition-Based Learning (OBL), that enhances the exploration of the search space through computing the opposition values of solutions generated by MFO. Moreover, such an approach increases the efficiency of MFO as multiple regions in the search space are investigated at the same time. The proposed algorithm (referred to as OBMFO) avoids the limitations of MFO (and other swarm intelligence algorithms) that result from the moving in the direction of the best solution, especially if this direction does not lead to the global optimum. Experiments are run using classical six benchmark functions to compare the performance of OBMFO against MFO. Moreover, OBMFO is used to solve the feature selection problem, using eight UCI datasets, in order to improve the classification performance through removing irrelevant and redundant features. The comparison results show that the OBMFO superiors to MFO for the tested benchmark functions. It also outperforms another three swarm intelligence algorithms in terms of the classification performance.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3017-3024
Number of pages8
ISBN (Electronic)9781728124858
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
CountryChina
CityXiamen
Period6.12.199.12.19

Keywords

  • Classification
  • Feature selection
  • Meta-heuristic
  • Moth-flame optimization (MFO)
  • Opposite-based learning (OBL)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Modelling and Simulation

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