An Arithmetic Optimization Algorithm with Dynamic State Estimation for Deadline-Constrained Workflow Scheduling

We present a novel Arithmetic Optimization Algorithm for solving a deadline-constrained many-objective workflow scheduling problem. Our approach incorporates an adaptive evolutionary state that dynamically balances exploration and exploitation within the search space. Additionally, we introduce an adaptive method for repairing infeasible solutions, further enhancing algorithm convergence and solution quality. An extensive experimental evaluation on workflows of real-world applications confirms the effectiveness of our approach.