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Found 8 papers, 7 papers with code
AbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems
Using this framework, we investigate components that were proposed in several popular DOP algorithms.
Multiobjective Evolutionary Component Effect on Algorithm behavior
In terms of decision space behavior, we see a diverse set of the trajectories of the STNs in the analytical artificial problems.
Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems
This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm.
Search Trajectories Networks of Multiobjective Evolutionary Algorithms
Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem.
Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition
MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicates.
MOEA/D with Adaptative Number of Weight Vectors
Thus, our adaptive mechanism mitigates problems related to the choice of the number of weight vectors in MOEA/D, increasing the final performance of MOEA/D by filling empty areas of the objective space while avoiding premature stagnation of the search progress.
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints.
MOEA/D with Random Partial Update Strategy
Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm.
