Elite opposition-based social spider optimization for solving benchmark problem

Abstract

Metaheuristic algorithms are powerful tools for solving complex optimization problems where traditional methods fail. The Social Spider Optimization (SSO) algorithm, inspired by the cooperative foraging behavior of spiders, is a notable swarm intelligence technique. However, it can be prone to premature convergence. This paper presents an enhanced variant, the Elite Opposition-Based Social Spider Optimization (EOSSO) algorithm, which integrates an elite opposition-based learning (OBL) strategy and an elite selection mechanism into the standard SSO framework. This integration aims to improve population diversity, enhance global exploration, and accelerate convergence. The performance of EOSSO is rigorously evaluated on a comprehensive set of 23 benchmark functions, including unimodal, multimodal, and fixed-dimension multimodal problems. Experimental results demonstrate that EOSSO significantly outperforms the standard SSO and other well-known metaheuristics in terms of solution accuracy, convergence speed, and stability. The algorithm exhibits a remarkable ability to escape local optima and refine solutions efficiently, proving its robustness and effectiveness as a high-performance optimizer for complex landscapes.