Implementation of Network Optimization and Resiliency Analysis Towards Mission Assurance

Abstract Network optimization and resiliency analysis are pivotal domains revealing network functionality, strength, and resilience. Despite their promise, these methodologies often encounter integration limitations, scalability issues, or functional gaps, prompting the need for further development. This paper explores the characteristics of network optimization and resiliency analysis, presenting existing methodologies, discussing their limitations, and proposing a new approach. Our proposed method integrates genetic algorithms (GAs) and weighted degradation analysis to address these shortcomings effectively. Our research emphasizes the integration of GAs for network optimization and weighted degradation analysis for resiliency assessment. Through a case study focusing on an electrical grid system, we elucidate the efficacy and challenges of our proposed methodologies. While GAs efficiently generate optimized networks, they encounter inconsistencies due to algorithmic issues. Similarly, weighted degradation analysis demonstrates forward propagation capabilities but faces challenges in assessing back-propagation issues adequately. Network optimization and resiliency analysis play a crucial role in understanding network characteristics, contributing to overall network assurance and mission success. This study underscores the importance of ongoing refinement and resolution of algorithmic issues to fully leverage these methodologies. Further research is essential to overcoming existing challenges and seamlessly integrating these methodologies into mission-critical systems, thereby enhancing network understanding and optimization capabilities.