The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

Constrained multiobjective optimization problems (CMOPs) are frequently encountered in
real-world applications, which usually involve constraints in both the decision and objective …

Solving constrained multiobjective optimization problems brings great challenges to an
evolutionary algorithm, since it simultaneously requires the optimization among several …

For solving constrained multiobjective optimization problems (CMOPs), many algorithms
have been proposed in the evolutionary computation research community for the past two …

In the multimodal multi-objective optimization problems (MMOPs), there exists more than
one Pareto optimal solutions in the decision space corresponding to the same location on …

For multi-objective optimization problems with irregular Pareto Fronts, most widely used
decomposition methods in MOEA/D (multi-objective evolutionary algorithms based on …

Multitask optimization (MTO) is a new optimization paradigm that leverages useful
information contained in multiple tasks to help solve each other. It attracts increasing …

Air traffic flow management (ATFM) plays a crucial role in efficient aviation. Most existing
studies assume the flight speed as constant throughout the trip, leading to ineffective fixed …

Performance of multi/many-objective evolutionary algorithms (MOEAs) based on
decomposition is highly impacted by the Pareto front (PF) shapes of multi/many-objective …

Balancing constraints and objective functions in constrained evolutionary multiobjective
optimization is not an easy task. Overemphasis on constraints satisfaction may easily lead to …