Improving Performance Insensitivity of Large-Scale Multiobjective Optimization via Monte Carlo Tree Search.

The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering can be modeled as LSMOPs; simultaneously, engineering applications require insensitivity in performance. This requirement typically means that the algorithm should not only produce good results in terms of performance for every run but also the performance of multiple runs should not fluctuate too much.

Manifold Interpolation for Large-Scale Multiobjective Optimization via Generative Adversarial Networks.

Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional genetic operators for solving LSMOPs have some deficiencies in dealing with the manifold, which often results in poor diversity, local optima, and inefficient searches.