Bio-Inspired Optimization Through Photosynthesis: A Novel Approach for Balancing Exploration and Exploitation in Complex Systems

Abstract

This paper presents the Photosynthesis-Inspired Optimization (PSIO) algorithm, an innovative metaheuristic that emulates the adaptive efficiency of plant photosynthesis. Inspired by chlorophyll’s dual role in light absorption and energy conversion, PSIO operates through two core mechanisms: the Photo-Intensification Mechanism, which focuses on refining high-potential solutions, and the Photosynthetic Pathway Diversification, which promotes thorough exploration of the solution space. These interconnected strategies enable PSIO to maintain a dynamic balance between exploration and exploitation, reducing the likelihood of premature convergence - a common challenge in complex optimization problems. Additionally, the algorithm incorporates an adaptive adjustment mechanism, akin to the photoprotective responses in plants, enhancing its flexibility and robustness across various optimization scenarios. The effectiveness of PSIO is validated through extensive benchmarking, consistently outperforming conventional metaheuristics in both convergence speed and solution quality. More specifically, PSIO consistently achieves lower total transmission costs with improvements ranging from $10\%$ to $25\%$ for small and large-scale problems.