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

TL;DR

This paper introduces a novel swarm optimization algorithm inspired by social-spider behavior, demonstrating high effectiveness in solving complex benchmark optimization problems through cooperative interactions modeled after biological laws.

Contribution

The Social Spider Optimization (SSO) algorithm is a new swarm intelligence method that mimics social-spider cooperative behavior, including gender-based roles and interactions, for enhanced optimization performance.

Findings

SSO outperforms several well-known evolutionary algorithms on benchmark functions.

The algorithm effectively searches for global optima in complex optimization landscapes.

SSO demonstrates robustness and high proficiency in diverse test scenarios.

Abstract

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.