A swarm optimization algorithm inspired in the behavior of the social-spider

By Erik Valdemar Cuevas Jiménez, Miguel Cienfuegos, Daniel Zaldivar, Marco Antonio Pérez Cisneros. Expert systems with applications, 2013

536 citations recorded by the source corpus.

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Genetics

View Paper: https://www.semanticscholar.org/paper/1447fe8dc5e08f4a6bad99d2730347dd5a134354

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Citations in the corpus

The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method (2016, methodology): “The individual sc and sb denotes the nearest member to i that holds a higher weight and the best individual of the entire population S, respectively. rm is a uniform random number, which is generated between [0,1], whereas PF is a threshold and is often set to be 0.7 (Cuevas et al. 2013).”
Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm (2016, unknown): “The Social spider optimization, inspired by the social behavior of a kind of spider, has been proposed recently [46].”
Metaheuristic research: a comprehensive survey (2018, methodology): “…Scatter Search and Path Relinking (Glover 1997) SSO Simplified Swarm Optimization (Yeh et al. 2015) SSOA Social Spider Optimization Algorithm (Cuevas et al. 2013) TLBO Teaching-Learning-Based Optimization (Rao et al. 2011) TS Tabu Search (Glover 1989) VCS Virus Colony Search (Li et al.…”
A social spider algorithm for global optimization (2015, methodology): “As to Social Spider Optimization (SSO) [25], diﬀer-ences lie in all aspects of the algorithm design.”