Ambidextrous Socio-Cultural Algorithms

José Lemus-Romani, Broderick Crawford, Ricardo Soto, Gino Astorga, Sanjay Misra, Kathleen Crawford, Giancarla Foschino, Agustín Salas-Fernández, Fernando Paredes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings
EditorsOsvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, Yeliz Karaca
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030588168
StatePublished - 2020
Event20th International Conference on Computational Science and Its Applications, ICCSA 2020 - Cagliari, Italy
Duration: 1 Jul 20204 Jul 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12254 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Computational Science and Its Applications, ICCSA 2020


  • Ambidextrous metaheuristics
  • Human-based algorithm
  • Social-inspired metaheuristics
  • Socio-cultural inspired metaheuristics
  • Teaching–learning-based optimization
  • Twitter optimization


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