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

Marcos Henrique Carvalho Silva1, André Vinícius Oliveira Maggio1, Paulo Alexandre Pizará Hayashida1

  • 1Electronic Systems Engineering Department, Escola Politécnica, Universidade de São Paulo, São Paulo, SP, Brazil.

Journal of Environmental Management
|March 20, 2026
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Summary
This summary is machine-generated.

Bio-inspired algorithms optimize internal combustion engine calibration for improved fuel efficiency and emissions. The Dumbo Octopus Algorithm (DOA) demonstrated effective control map generation, maintaining low error across operating conditions.

Keywords:
CalibrationEnergy efficiencyInternal combustion engineMetaheuristicsOptimization algorithms

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Area of Science:

  • Automotive Engineering
  • Computational Intelligence
  • Combustion Science

Background:

  • Modern internal combustion engines face conflicting demands for high torque, fuel efficiency, and emissions compliance.
  • Engine calibration is complex due to nonlinear relationships, actuator interactions, and operating variability.
  • Conventional gradient-based methods struggle with the multimodality and nonconvexity of engine calibration problems.

Purpose of the Study:

  • To apply bio-inspired metaheuristic algorithms for multi-target internal combustion engine calibration.
  • To benchmark the performance of five derivative-free global search algorithms.
  • To develop an automated calibration workflow for efficiency-oriented control maps.

Main Methods:

  • Constructed a high-fidelity surrogate model using Principal Component Analysis (PCA) and Gaussian Process Regression (GPR) on engine data.
  • Validated the surrogate model using k-fold cross-validation for accurate prediction of torque, brake thermal efficiency (BTE), and exhaust gas temperature.
  • Benchmarked five metaheuristic optimizers: Meerkat Optimization Algorithm (MOA), Dumbo Octopus Algorithm (DOA), Pufferfish Optimization Algorithm (POA), Hybrid Jellyfish Search-Particle Swarm Optimization (HJSPSO), and Dendritic Growth Optimization (DGO).

Main Results:

  • The Dumbo Octopus Algorithm (DOA) was utilized in an automated workflow to generate efficiency-oriented control maps.
  • The DOA-based calibration achieved percentage errors up to 6% relative to reference targets across tested conditions.
  • Analysis focused on high-efficiency setpoints with Brake Thermal Efficiency (BTE) values of at least 30%.

Conclusions:

  • Bio-inspired metaheuristic algorithms offer a robust approach to complex, multi-objective engine calibration.
  • The Dumbo Octopus Algorithm (DOA) proved effective for generating optimized engine control maps.
  • This methodology enables simultaneous optimization of engine performance metrics while meeting stringent requirements.