Innovation-driven development path of the new energy industry based on immune optimisation and simulated annealing algorithm

This study constructs a hybrid intelligent decision-making model that combines immune optimisation algorithm and simulated annealing algorithm to address challenges faced by innovative new energy industry systems, such as ambiguous path planning and suboptimal resource allocation.Comparative experiments have shown that in the product development cycle, the average task duration has been reduced from 43.6 days using traditional methods to 34.6 days, resulting in a 20.6% increase in efficiency.In terms of supply chain optimisation, for silicone suppliers, the efficiency of supplier identification process has increased by 34.5%, while for other key raw material suppliers, the average search time has been reduced by 36%.This model combines the 'global search capability' of immune algorithms with the 'local optimisation' of simulated annealing, effectively balancing technological innovation and market efficiency, providing a scientific path planning and decision support tool for innovation driven development in the new energy industry.