Research Area: Metaheuristic Computing
Swarm intelligence (SI) is a research feld which has recently attracted the attention of several scientifc communities. An SI approach tries to characterize the collective behavior of animal or insect groups to build a search strategy.Tese methods consider biological systems, which can be modeled as optimization processes to a certain extent. Te Social Spider Optimization (SSO) is a novel swarm algorithm that is based on the cooperative characteristics of the social spider. In SSO, search agents represent a set of spiders which collectively move according to the biological behavior of the colony. In most of SI algorithms, all individuals are modeled considering the same properties and behavior. In contrast, SSO defnes two diferent search agents: male and female. Therefore, according to the gender, each individual is conducted by using a diferent evolutionary operation which emulates its biological role in the colony. Tis individual categorization allows reducing critical faws present in several SI approaches such as incorrect exploration-exploitation balance and premature convergence. Afer its introduction, SSO has been modifed and applied in several engineering domains. In this paper, the state of the art, improvements, and applications of the SSO are reviewed.
Keywords:
swarm intelligence
scientifc communities
animal
insect
social spider optimization
exploration
exploitation balance
premature convergence
Author(s) Name: Alberto Luque-Chang, Erik Cuevas , Fernando Fausto, Daniel Zald-var , and Marco Perez
Journal name: Mathematical Problems in Engineering
Conferrence name:
Publisher name: Hindawi
DOI: 10.1155/2018/6843923
Volume Information: Volume 2018
