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Found 10 papers, 4 papers with code
On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem
We contribute to the efficient approximation of the Pareto-set for the classical $\mathcal{NP}$-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation.
MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
It is able to efficiently model and exploit LE sets in MMMOO problems.
To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes
Simultaneously visualizing the decision and objective space of continuous multi-objective optimization problems (MOPs) recently provided key contributions in understanding the structure of their landscapes.
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress.
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization.
One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes
Therefore, we build on the GFH approach but apply a new technique for approximating the location of locally efficient points and using the divergence of the multi-objective gradient vector field as a robust second-order condition.
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras).
Dynamic Bi-Objective Routing of Multiple Vehicles
In practice, e. g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests.
Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms
Therefore, we propose a postprocessing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of generated solutions after optimization in order to select a uniformly distributed subset of nondominated solutions from the archive.
