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Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator)...
The Robust Vehicle Routing Problem with Time Windows has been gaining popularity over the past few years due to its focus on tackling uncertainty inherent to real world problems. Most of the current approaches in generating robust solutions require prior knowledge on the uncertainties, such as uncertainties in travel time. Hence, they are less than favorable to use in the absence of data, i.e., in...
The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this...
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