Seeking and leveraging alternative variable dependency concepts in gray-box-elusive bimodal land-use allocation problems
This work addresses a specific real-world multi-objective optimization problem in land-use allocation, offering incremental improvements for environmental planning applications.
The authors tackled the challenge of solving gray-box-elusive bimodal land-use allocation problems, where standard variable dependency techniques fail, by proposing problem-dedicated variable dependency concepts and three novel crossover operators, which significantly improved the effectiveness of NSGA-II and MOEA/D optimizers in real-world test cases.
Solving land-use allocation problems can help us to deal with some of the most urgent global environmental issues. Since these problems are NP-hard, effective optimizers are needed to handle them. The knowledge about variable dependencies allows for proposing such tools. However, in this work, we consider a real-world multi-objective problem for which standard variable dependency discovery techniques are inapplicable. Therefore, using linkage-based variation operators is unreachable. To address this issue, we propose a definition of problem-dedicated variable dependency. On this base, we propose obtaining masks of dependent variables. Using them, we construct three novel crossover operators. The results concerning real-world test cases show that introducing our propositions into two well-known optimizers (NSGA-II, MOEA/D) dedicated to multi-objective optimization significantly improves their effectiveness.