An Enhanced Social Spider Colony Optimization for Global Optimization

Farouq Zitouni, Saad Harous, Ramdane Maamri

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

Abstract

An improved variant of the social spider optimization algorithm is introduced. It is inspired by the hunting and mating behaviors of spiders in nature. We call it enhanced social spider colony optimization (ESSCO). The performance of ESSCO is evaluated using the benchmark CEC 2020. To validate the proposed algorithm, the obtained statistical results are compared to eleven recent state-of-the-art metaheuristic algorithms. The comparative study shows the competitiveness of ESSCO in finding efficient solutions to the considered test functions.

Original languageEnglish
Title of host publicationNetworking, Intelligent Systems and Security - Proceedings of NISS 2021
EditorsMohamed Ben Ahmed, Horia-Nicolai L. Teodorescu, Tomader Mazri, Parthasarathy Subashini, Anouar Abdelhakim Boudhir
PublisherSpringer Science and Business Media Deutschland GmbH
Pages775-793
Number of pages19
ISBN (Print)9789811636363
DOIs
Publication statusPublished - 2022
Event4th International Conference on Networking, Intelligent Systems and Security, NISS 2021 - Kenitra, Morocco
Duration: Apr 1 2020Apr 2 2020

Publication series

NameSmart Innovation, Systems and Technologies
Volume237
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference4th International Conference on Networking, Intelligent Systems and Security, NISS 2021
Country/TerritoryMorocco
CityKenitra
Period4/1/204/2/20

Keywords

  • CEC 2020
  • Global optimization
  • Metaheuristic algorithms
  • Social spiders
  • Swarm intelligence
  • Test functions

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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