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

Posted: Oct 28, 2025

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

Abstract

Traditional computational models have predominantly drawn inspiration from animal cognition and behavior, yielding powerful paradigms such as neural networks, genetic algorithms, and swarm intelligence. However, the computational potential of plant intelligence remains largely unexplored despite plants' remarkable abilities to solve complex optimization problems in resource allocation, structural stability, and environmental adaptation. This paper introduces Phytomorphic Computing as a novel framework that translates botanical growth principles into computational algorithms for network optimization. Plants exhibit sophisticated problem-solving capabilities through decentralized decision-making, emergent pattern formation, and adaptive resource management. Unlike animal-inspired approaches that often prioritize speed and immediate response, plant-inspired algorithms emphasize sustainability, resilience, and long-term optimization. Our research addresses the fundamental question: How can the growth strategies and environmental adaptation mechanisms of vascular plants inform the design of more robust and adaptive computational networks? We propose three core botanical principles as computational foundations: (1) meristematic programming for distributed growth control, (2) tropic response