NATURE-INSPIRED ALGORITHMS: A COMPREHENSIVE REVIEW OFPARADIGMS, MECHANISMS, AND EMERGING TRENDS IN OPTIMIZATION

Abstract

Nature-inspired algorithms (NIAs) have emerged as powerful paradigms for solving complex optimization problems that defy traditional mathematical approaches. Drawing inspiration from billions of years of evolutionary refinement, collective intelligence, and adaptive behaviors observed in biological systems, these algorithms have transformed the landscape of computational problemsolving. This article presents a comprehensive review of nature-inspired algorithms, systematically categorizing them into evolutionary algorithms, swarm intelligence, plant-based approaches, and physics-inspired methods. We analyze their foundational mechanisms, particularly the critical exploration-exploitation trade-off that governs their performance. The review examines recent advancements, including hybridization strategies, transfer learning integration, and adaptive parameter control mechanisms. Special attention is given to the underexplored potential of plantinspired algorithms, which constitute only 9.7% of the literature yet demonstrate competitive performance on benchmark problems. Through comparative analysis and visual frameworks, we identify persistent challenges—theoretical foundation gaps, scalability limitations, and the "algorithmic metaphor epidemic"—while proposing future research directions. This work serves as both an introductory guide for new researchers and a critical analysis for practitioners seeking to understand the current landscape and future trajectory of nature-inspired optimization.