Optimistic variants of single-objective bilevel optimization for evolutionary algorithms
This work addresses decision-making problems in real-world applications for the evolutionary computation community, but it is incremental as it builds on existing optimistic and pessimistic approaches.
The paper tackles single-objective bilevel optimization problems, which are non-convex and NP-hard, by proposing a partial nested evolutionary approach with local heuristic search and an extreme optimistic variant, achieving competitive results in benchmark tests.
Single-objective bilevel optimization is a specialized form of constraint optimization problems where one of the constraints is an optimization problem itself. These problems are typically non-convex and strongly NP-Hard. Recently, there has been an increased interest from the evolutionary computation community to model bilevel problems due to its applicability in the real-world applications for decision-making problems. In this work, a partial nested evolutionary approach with a local heuristic search has been proposed to solve the benchmark problems and have outstanding results. This approach relies on the concept of intermarriage-crossover in search of feasible regions by exploiting information from the constraints. A new variant has also been proposed to the commonly used convergence approaches, i.e., optimistic and pessimistic. It is called extreme optimistic approach. The experimental results demonstrate the algorithm converges differently to known optimum solutions with the optimistic variants. Optimistic approach also outperforms pessimistic approach. Comparative statistical analysis of our approach with other recently published partial to complete evolutionary approaches demonstrates very competitive results.