QMOEA: A Q-learning-based multiobjective evolutionary algorithm for solving time-dependent green vehicle routing problems with time windows
The vehicle routing problem with time windows (VRPTW) is critical in the fields of operations
research and combinatorial optimization. To promote research on the multiobjective …
research and combinatorial optimization. To promote research on the multiobjective …
A novel adaptive weight algorithm based on decomposition and two-part update strategy for many-objective optimization
Decomposition-based multi-objective evolutionary algorithm (MOEA/D) has good
performance in solving multi-objective problems (MOPs) but poor performance in solving …
performance in solving multi-objective problems (MOPs) but poor performance in solving …
Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization
The environments of the dynamic multiobjective optimization problems (DMOPs), such as
Pareto optimal front (POF) or Pareto optimal set (POS), usually frequently change with the …
Pareto optimal front (POF) or Pareto optimal set (POS), usually frequently change with the …
Penalty and prediction methods for dynamic constrained multi-objective optimization
Dynamic constrained multi-objective optimization problems (DCMOPs) involve objective
functions and constraints that vary over time, requiring optimization algorithms to track the …
functions and constraints that vary over time, requiring optimization algorithms to track the …
Microgrid energy management system with degradation cost and carbon trading mechanism: A multi-objective artificial hummingbird algorithm
Microgrid is an important way to optimize the distributed power generation and its optimal
scheduling to ensure reliable and economical operation. This study constructs a multi …
scheduling to ensure reliable and economical operation. This study constructs a multi …
Decomposition-based multiobjective optimization for nonlinear equation systems with many and infinitely many roots
Although the development of Pareto-dominance-based multiobjective optimization
algorithms has enabled the solution of nonlinear equation systems, few studies have been …
algorithms has enabled the solution of nonlinear equation systems, few studies have been …
A novel two-phase evolutionary algorithm for solving constrained multi-objective optimization problems
It is challenging to balance convergence and diversity in constrained multi-objective
optimization problems (CMOPs) since the complex constraints will disperse the feasible …
optimization problems (CMOPs) since the complex constraints will disperse the feasible …
An effective and efficient evolutionary algorithm for many-objective optimization
In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary
algorithm performs in terms of converging its solutions into the Pareto front and also …
algorithm performs in terms of converging its solutions into the Pareto front and also …
A dual-population-based evolutionary algorithm for multi-objective optimization problems with irregular Pareto fronts
X Zhong, X Yao, D Gong, K Qiao, X Gan, Z Li - Swarm and Evolutionary …, 2024 - Elsevier
When solving multi-objective optimization problems (MOPs) with irregular Pareto fronts (eg,
disconnected, degenerated, inverted) via evolutionary algorithms, a critical issue is how to …
disconnected, degenerated, inverted) via evolutionary algorithms, a critical issue is how to …
A dynamic multiobjective optimization algorithm based on decision variable relationship
Z Hu, Z Li, L Wei, H Sun, X Ma - Neural Computing and Applications, 2023 - Springer
Dynamic multiobjective optimization problems exist in daily life and industrial practice. The
objectives of dynamic multiobjective optimization problems conflict with each other. In most …
objectives of dynamic multiobjective optimization problems conflict with each other. In most …