Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation

In this paper, we present Atomic Energy Optimization (AEO), a novel meta-heuristic optimization technique inspired by atomic energy dynamics and the process of static electricity dissipation. AEO models optimization by mimicking the energy accumulation, transfer, and dissipation behaviors observed in atoms, particularly during processes involving electrostatic charge and discharge. Each solution in AEO is represented as an atom with electrons, where its energy state corresponds to solution quality. Solutions interact, exchanging and dissipating energy in a manner analogous to static electricity buildup and release—such as when a plastic rod is rubbed to accumulate charge, then gradually neutralized upon contact with another surface. This energy-driven model allows AEO to balance exploration and exploitation dynamically, enhancing its ability to navigate com-plex, multimodal landscapes. The paper delves into key AEO mechanisms, including energy transfer, dissipation, and parameter sensitivity, exploring their impact on the algorithm’s performance across challenging benchmarks. Notable applications of AEO to problems like the Rastrigin function and the Traveling Salesman Problem (TSP) showcase its effectiveness, with experimental results demonstrating superior convergence and robustness compared to traditional methods like Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Through extensive experimentation, AEO achieved up to a 20% faster convergence rate and a 15% improvement in solution quality over peer methods, demonstrating its superiority in solving benchmark optimization problems.