<p>Membrane computing, particularly optimization spiking neural P systems (OSNPS), has emerged as a powerful paradigm for solving complex optimization problems. Despite its potential, existing OSNPS variants face significant challenges, including limited initial population diversity, premature convergence, and an insufficient balance between exploration and exploitation. These limitations hinder the algorithm’s ability to achieve optimal solutions efficiently and reliably, highlighting a critical gap in the current state of the art. To address these challenges, we propose An Optimization Spiking Neural P Systems with Synaptic Plasticity, named OSNPSsp, which integrates principles from biological systems and non-linear dynamics. Specifically, OSNPSsp incorporates two key innovations: a chaotic initialization strategy to enhance initial population diversity and a Hebbian learning-enhanced probability update mechanism to adaptively reinforce promising solutions. The theoretical significance of this work lies in the integration of non-linear dynamics and biological synaptic plasticity into the membrane computing framework, establishing a more principled approach to balancing exploration and exploitation. Practical evaluations on the 0/1 knapsack problem demonstrate that OSNPSsp significantly outperforms state-of-the-art algorithms, including GQA, NQEA, OSNPS, AOSNPS, and DAOSNPS. The results confirm that the proposed model not only delays premature convergence but also ensures superior solution quality and computational robustness, providing a scalable and efficient solution for complex combinatorial optimization challenges.</p>