TY - GEN
T1 - Ambidextrous Socio-Cultural Algorithms
AU - Lemus-Romani, José
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Astorga, Gino
AU - Misra, Sanjay
AU - Crawford, Kathleen
AU - Foschino, Giancarla
AU - Salas-Fernández, Agustín
AU - Paredes, Fernando
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Ambidextrous metaheuristics
KW - Human-based algorithm
KW - Social-inspired metaheuristics
KW - Socio-cultural inspired metaheuristics
KW - Teaching–learning-based optimization
KW - Twitter optimization
UR - http://www.scopus.com/inward/record.url?scp=85092674461&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58817-5_65
DO - 10.1007/978-3-030-58817-5_65
M3 - Conference contribution
AN - SCOPUS:85092674461
SN - 9783030588168
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 923
EP - 938
BT - Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Garau, Chiara
A2 - Blecic, Ivan
A2 - Taniar, David
A2 - Apduhan, Bernady O.
A2 - Rocha, Ana Maria A.C.
A2 - Tarantino, Eufemia
A2 - Torre, Carmelo Maria
A2 - Karaca, Yeliz
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Computational Science and Its Applications, ICCSA 2020
Y2 - 1 July 2020 through 4 July 2020
ER -