Abstract
Swarm intelligence (SI) is a research field which has recently attracted the attention of several scientific communities. An SI approach tries to characterize the collective behavior of animal or insect groups to build a search strategy. These methods consider biological systems, which can be modeled as optimization processes to a certain extent. The 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 defines two different search agents: male and female. Therefore, according to the gender, each individual is conducted by using a different evolutionary operation which emulates its biological role in the colony. This individual categorization allows reducing critical flaws present in several SI approaches such as incorrect exploration-exploitation balance and premature convergence. After its introduction, SSO has been modified and applied in several engineering domains. In this paper, the state of the art, improvements, and applications of the SSO are reviewed.
Highlights
Swarm intelligence (SI) algorithms have attracted the attention of researchers due to their powerful and efficient performance for solving optimization problems
The Social Spider Optimization (SSO) [3] proposed by Erik Cuevas et al, in 2013, is a population-based algorithm that simulates the cooperative behavior of the social spider
Where C and D are called condition and decision features and POSC(D) is the positive region that contains all the objects of U that can be classified in classes of U/D using information of C, which is used to evaluate the performance of the solutions
Summary
Swarm intelligence (SI) algorithms have attracted the attention of researchers due to their powerful and efficient performance for solving optimization problems. These algorithms are developed by the combination between randomness and deterministic rules, emulating animal groups in the nature swarms [1]. Insects and animal groups provide a rich set of metaphors to develop new swarm algorithms; these entities are complex systems with individuals that reproduce different behaviors depending on its biological role such as gender, type, or size [2]. Most of SI algorithms model generic individuals with the same simple rule of behavior Under such conditions, it is not possible to include new operators that reproduce complex biological behaviors such as task-division and gender responsibility. The Social Spider Optimization (SSO) [3] proposed by Erik Cuevas et al, in 2013, is a population-based algorithm that simulates the cooperative behavior of the social spider
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