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Decomposition with adaptive composite norm for evolutionary multi-objective combinatorial optimization
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes
a multi-objective problem into a series of single-objective subproblems for collaborative …
a multi-objective problem into a series of single-objective subproblems for collaborative …
A Survey of Multi-objective Evolutionary Algorithm Based on Decomposition: Past and Future
K Li - IEEE Transactions on Evolutionary Computation, 2024 - ieeexplore.ieee.org
Decomposition has been the mainstream approach in the classic mathematical
programming for multi-objective optimization and multi-criterion decision-making. However …
programming for multi-objective optimization and multi-criterion decision-making. However …
LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
Multiobjective optimization problems (MOPs) are prevalent in machine learning, with
applications in multi-task learning, learning under fairness or robustness constraints, etc …
applications in multi-task learning, learning under fairness or robustness constraints, etc …
Relation between objective space normalization and weight vector scaling in decomposition-based multiobjective evolutionary algorithms
Real-world multiobjective optimization problems (MOPs) usually have conflicting and
differently scaled objectives. To deal with such problems, objective space normalization is …
differently scaled objectives. To deal with such problems, objective space normalization is …
A dynamic parameter tuning strategy for decomposition-based multi-objective evolutionary algorithms
J Zheng, J Ning, H Ma, Z Liu - Applied Sciences, 2024 - mdpi.com
The penalty-based boundary cross-aggregation (PBI) method is a common decomposition
method of the MOEA/D algorithm, but the strategy of using a fixed penalty parameter in the …
method of the MOEA/D algorithm, but the strategy of using a fixed penalty parameter in the …
The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts
It has been acknowledged that dominance-resistant solutions (DRSs) extensively exist in the
feasible region of multi-objective optimization problems. Recent studies show that DRSs can …
feasible region of multi-objective optimization problems. Recent studies show that DRSs can …
Objective Space Normalization in Evolutionary Multi-Objective Optimization
L He - 2023 - search.proquest.com
Objective space normalization is crucial for handling real-world multiobjective optimization
problems (MOPs) with differently-scaled objectives, as it allows most multiobjective …
problems (MOPs) with differently-scaled objectives, as it allows most multiobjective …