Design of an iterative method for environmental-sustainable development: Integrating bioinspired computing techniques

Abstract

The need for sustainable development has grown in response to global environmental, social, and economic challenges. Conventional computational methods frequently struggle to address the complex nature of the Sustainable Development Goals (SDGs), lacking the ability to balance global search with local optimization and failing to prioritize goals related to sustainability. To address these restrictions, this work introduces the Integrated Bioinspired Computing Model for Sustainable Development (IBCMSD). By combining Genetic Algorithms (GAs), Artificial Neural Networks (ANNs), and Ant Colony Optimization (ACO), a cohesive hybrid model is developed that improves exploration and exploitation, balance for increased efficiency, and solution quality. It is implemented on High-Performance Computing (HPC) clusters to ensure scalability and resilience when dealing with complicated optimization challenges. Furthermore, using a multidisciplinary co-design method completes the model with multiple views, increasing its relevance and applicability in real-world circumstances. IBCMSD makes a significant contribution to computational sustainability by leveraging bioinspired computing, potentially enabling informed decision-making and SDG accomplishment across multiple domains.