Fixed points of set-valued mappings in quasilinear metric spaces via swarm intelligence

Abstract

Fixed point theory in quasilinear metric spaces plays an important role in programming and data science. However, determining the existence and approximation of fixed points becomes particularly challenging when dealing with set-valued mappings. In this article, we introduce the use of swarm intelligence algorithms as an alternative approach for approximating fixed points in quasilinear metric spaces (Rⁿ). To evaluate their effectiveness, five promising swarm intelligence algorithms are employed and analyzed. The results indicate that swarm intelligence provides a robust and competitive framework for fixed point approximation in quasilinear metric spaces.