Design of an Efficient Model for Quad Bio-Inspired Optimization for Securing and Scheduling Smart Grids

The dynamic and decentralized character of smart grids presents hurdles for traditional optimization techniques, underscoring the need for novel approaches that may effectively tackle these issues. Some of the algorithms, such as Particle Swarm Optimization and Genetic Algorithms, show shortcomings when it comes to successfully navigating the complex terrain of smart grid optimization challenges. These approaches frequently have scaling problems, limited solution space exploration, and premature convergence when used on large-scale systems. This research suggests a novel framework to address these issues by combining the intelligence of several bio-inspired optimization algorithms, including the Bacterial Foraging Optimization (BFO), Firefly Algorithm (FA), Bat Algorithm (BA), and Cuckoo Search (CS). These algorithms, which use the concepts of chemotaxis, social attraction, brood parasitism, and echolocation, provide special advantages in exploring and utilizing the different solution spaces inherent in smart grid optimization challenges. Integrating BFO, FA, CS, and BA allows for robust and adaptive optimization of various smart grid applications, including intrusion detection system configuration, distributed energy resource management, maintenance scheduling, and cybersecurity resource allocation. By capitalizing on the complementary strengths of each algorithm, the proposed framework achieves superior performance over traditional optimization approaches in terms of solution quality, convergence speed, and scalability.