Advances in Mathematics: A Dual-Population Based Two-Archive Coevolution Algorithm for Constrained Multi-Objective Optimization Problems

Strategic Insights -

A recent study published in the Journal of Engineering has made significant advancements in the field of Mathematics, specifically in the area of constrained multi-objective optimization problems (CMOPs). Researchers from Liaoning Technical University have proposed a dual-population based two-archive coevolution algorithm (DPTAC) to tackle the challenges of CMOPs. This innovative algorithm aims to balance the objectives and constraints better, improving the convergence of the population and discovering more feasible regions.

Key Takeaways:

  • The DPTAC algorithm proposes a dual-population based approach, where the main population evolves towards the true Pareto front while accounting for the original problem, and the auxiliary population ignores the constraints and approximates the unconstrained Pareto front.
  • The algorithm uses a two-archive strategy to store potentially valuable non-dominated infeasible solutions and non-dominated solutions generated by the evolution of the main population and the auxiliary population respectively.
  • The removal mechanism is introduced and integrated into the auxiliary population to reduce computational resource waste, allowing the main population to have more computational resources in the late stage of evolution.
  • Experimental results demonstrate that DPTAC outperforms 9 state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) across 5 test suites comprising 62 benchmark functions and 6 real-world problems.
  • The research has been peer-reviewed and has been published in the Journal of Engineering.
  • The algorithm has been developed by a team of researchers from Liaoning Technical University, including Shijie Zhao, Miao Chen, Tianran Zhang, and Lei Zhang.

Statistics:

  • 9 state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) were outperformed by DPTAC in experimental results.
  • 62 benchmark functions and 6 real-world problems were used in the performance evaluation of DPTAC.
  • 5 test suites were used in the experimental results, demonstrating the superiority of DPTAC.
  • The removal mechanism was integrated into the auxiliary population to reduce computational resource waste.

Sources:

  • A Dual-population Based Two-archive Coevolution Algorithm for Constrained Multi-objective Optimization Problems. Engineering Applications of Artificial Intelligence, 2025;158.
  • NewsRx. New Findings from Liaoning Technical University Describe Advances in Mathematics (A Dual-population Based Two-archive Coevolution Algorithm for Constrained Multi-objective Optimization Problems). Journal of Engineering. October 20, 2025; p 1830.