Robust optimization over time: a critical review
D Yazdani, MN Omidvar, D Yazdani… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Robust optimization over time (ROOT) is the combination of robust optimization and dynamic
optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can …
optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can …
A practical tutorial on solving optimization problems via PlatEMO
PlatEMO is an open-source platform for solving complex optimization problems, which
provides a variety of metaheuristics including evolutionary algorithms, swarm intelligence …
provides a variety of metaheuristics including evolutionary algorithms, swarm intelligence …
Multi-strategy dynamic multi-objective evolutionary algorithm with hybrid environmental change responses
A key issue in evolutionary algorithms for dynamic multi-objective optimization problems
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …
Evolutionary dynamic constrained multiobjective optimization: Test suite and algorithm
G Chen, Y Guo, Y Wang, J Liang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world
applications and gain increasing attention in the evolutionary computation community. To …
applications and gain increasing attention in the evolutionary computation community. To …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
Large language model for multi-objective evolutionary optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
Temporal distribution-based prediction strategy for dynamic multi-objective optimization assisted by GRU neural network
X Hou, F Ge, D Chen, L Shen, F Zou - Information Sciences, 2023 - Elsevier
To solve dynamic multi-objective optimization problems, evolutionary algorithms must be
capable of quickly and accurately tracking the changing Pareto front such that they can …
capable of quickly and accurately tracking the changing Pareto front such that they can …
Interaction-based prediction for dynamic multiobjective optimization
Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due
to the change of optimal solutions or Pareto front with time. Learning-based methods are …
to the change of optimal solutions or Pareto front with time. Learning-based methods are …
A framework based on historical evolution learning for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-
world applications and have received considerable attention in recent years. During the …
world applications and have received considerable attention in recent years. During the …
Algorithm evolution using large language model
Optimization can be found in many real-life applications. Designing an effective algorithm for
a specific optimization problem typically requires a tedious amount of effort from human …
a specific optimization problem typically requires a tedious amount of effort from human …