Phytomorphic Computing: A Bio-Inspired Framework for Adaptive Network Topology Optimization Using Plant Growth Algorithms

Posted: Apr 24, 2024

• Dr. Elara Vance
• Prof. Kenji Tanaka
• Dr. Isabella Rossi

Abstract

This paper introduces Phytomorphic Computing, a novel computational paradigm inspired by the adaptive growth patterns and resource allocation strategies of vascular plants. Unlike traditional network optimization approaches that rely on mathematical programming or evolutionary algorithms, our framework models computational processes after the phototropic, gravitropic, and hydrotropic responses observed in plant systems. We develop a comprehensive mathematical model that translates plant hormonal signaling pathways (auxin, cytokinin, gibberellin) into computational heuristics for dynamic network reconfiguration. The core innovation lies in treating network nodes as meristematic tissues and data flows as nutrient transport, enabling emergent self-organization through distributed decision-making processes. Our methodology implements three key botanical principles: apical dominance for hierarchical control, phyllotaxis for optimal spatial distribution, and mycorrhizal networks for distributed intelligence. We validate our approach through extensive simulations on three distinct problem domains: adaptive content delivery networks, emergency communication systems during natural disasters, and decentralized IoT coordination. Results demonstrate that phytomorphic algorithms achieve 27-42% improvement in network resilience compared to conventional optimization methods while reducing computational overhead by 18-31%. The framework exhibits unique emergent properties including graceful degradation under stress, spontaneous recovery from partitions, and environmentally-aware resource allocation. This research represents a significant departure from animal-inspired computational models (neural networks, swarm intelligence) by exploring the untapped potential of plant intelligence for solving complex distributed computing challenges. Our findings suggest that plant-inspired algorithms offer particularly strong advantages in scenarios requiring long-term stability, resource conservation, and adaptation to slowly-changing environmental conditions.