Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm

Atheer Bassel, Md. Jan Nordin

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

1 Citation (Scopus)

Abstract

The Glowworm Swarm Optimization (GSO) is a population-based metaheuristic algorithm for optimization problems. Limitations of GSO are shown at the convergence speed and a weakness in the capability of global search which need to be improved. Thus, Memory Mechanism and Mutation for Glowworm Swarm Optimization (MMGSO) are proposed in this study to improve the GSO performance at the reported aspects. The proposed method is examined on Unimodal and Multimodal benchmark functions to prove the productivity of the MMGSO algorithm regarding to three metrics which are solution quality, convergence speed and robustness. The results of MMGSO are analyzed and compared with the basic GSO to show the efficiency of the proposed method.

Original languageEnglish
Title of host publication2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042289
DOIs
Publication statusPublished - 1 Mar 2017
Event7th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2017 - Las Vegas, United States
Duration: 9 Jan 201711 Jan 2017

Other

Other7th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2017
CountryUnited States
CityLas Vegas
Period9/1/1711/1/17

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Keywords

  • Glowworm Swarm Optimization
  • Memory less
  • Metaheuristic algorithm
  • Mutation
  • Optimization

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Bassel, A., & Nordin, M. J. (2017). Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm. In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017 [7868403] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCWC.2017.7868403

Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm. / Bassel, Atheer; Nordin, Md. Jan.

2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7868403.

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

Bassel, A & Nordin, MJ 2017, Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm. in 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017., 7868403, Institute of Electrical and Electronics Engineers Inc., 7th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2017, Las Vegas, United States, 9/1/17. https://doi.org/10.1109/CCWC.2017.7868403
Bassel A, Nordin MJ. Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm. In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7868403 https://doi.org/10.1109/CCWC.2017.7868403
Bassel, Atheer ; Nordin, Md. Jan. / Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017. Institute of Electrical and Electronics Engineers Inc., 2017.
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