Balancing torque, efficiency, and emissions: A framework for engine optimization using modern metaheuristics

Modern internal combustion engines must satisfy increasingly strict and often conflicting requirements: high torque demand must be delivered without sacrificing fuel efficiency, while emissions compliance depends on maintaining suitable exhaust thermal conditions for effective aftertreatment operation. These objectives are strongly coupled and highly nonlinear, so improving one metric can deteriorate others, and the feasible region is further constrained by complex actuator interactions and operating-regime variability. To address this challenge, this study applies a suite of recent bio-inspired metaheuristic algorithms to multi-target engine calibration, leveraging their derivative-free global search capability to handle multimodality, nonconvexity, and black-box constraints that limit conventional gradient-based tuning. A high-fidelity surrogate model was first constructed from a public engine dataset using Principal Component Analysis (PCA) and Gaussian Process Regression (GPR) and validated via k-fold cross-validation, enabling fast and accurate prediction of torque, brake thermal efficiency (BTE), and exhaust gas temperature as the fitness function. Five optimizers were then benchmarked in terms of solution quality and convergence behavior in this realistic calibration setting: Meerkat Optimization Algorithm (MOA), Dumbo Octopus Algorithm (DOA), Pufferfish Optimization Algorithm (POA), Hybrid Jellyfish Search-Particle Swarm Optimization (HJSPSO), and Dendritic Growth Optimization (DGO). Finally, an automated DOA-based calibration workflow was used to generate efficiency-oriented control maps. Across all tested operating conditions, the DOA-based calibration maintained percentage errors of up to 6% relative to all reference targets, with the analysis focused on high-efficiency setpoints, as indicated by BTE reference values of at least 30%.