Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Journal of Global Optimization
The organization of work in social insect colonies
Complexity - Special issue: Selection, tinkering, and emergence in complex networks
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Engineering Optimization: An Introduction with Metaheuristic Applications
Engineering Optimization: An Introduction with Metaheuristic Applications
Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Applied Soft Computing
Diversity enhanced particle swarm optimization with neighborhood search
Information Sciences: an International Journal
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
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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.