Benchmarking and solving dynamic constrained problems

Many real-world dynamic optimisation problems have constraints, and in certain cases not only the objective function changes over time, but the constraints also change as well. However, in academic research there is not many research on continuous dynamic constrained optimization, and particularly there is little research on whether current numerical dynamic optimization algorithms would work well in dynamic constrained environments nor there is any numerical dynamic constrained benchmark problems. In this paper, we firstly investigate the characteristics that might make a dynamic constrained problems difficult to solve by existing dynamic optimization algorithms. We then introduce a set of numerical dynamic benchmark problems with these characteristics. To verify our hypothesis about the difficulty of these problems, we tested several canonical dynamic optimization algorithms on the proposed benchmarks. The test results confirm that dynamic constrained problems do have special characteristics that might not be solved effectively by some of the current dynamic optimization algorithms. Based on the analyses of the results, we propose a new algorithm to improve the performance of current dynamic optimization methods in solving numerical dynamic constrained problems. The test results show that the proposed algorithm achieves superior results compared to the tested existing dynamic optimization algorithms.