A Social Spider Algorithm for Global Optimization

TL;DR

The paper introduces a novel Social Spider Algorithm inspired by social spiders' foraging behavior, which effectively solves global optimization problems and outperforms existing metaheuristics on benchmark tests.

Contribution

It presents a new social animal foraging strategy model for optimization and provides parameter sensitivity analysis for the proposed algorithm.

Findings

Superior performance on benchmark functions

Effective parameter guidelines established

Outperforms state-of-the-art metaheuristics

Abstract

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The Social Spider Algorithm is evaluated by a series of widely-used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.