In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-
II) has attracted extensive research interests, and it is still one of the hottest research …

Both convergence and diversity are crucial to evolutionary many-objective optimization,
whereas most existing dominance relations show poor performance in balancing them, thus …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …

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 …

When optimizing many-objective problems (MaOP), the same strategy might behave
differently when facing problems with different features. Therefore, obtaining problem …

Objectives optimization and constraints satisfaction are two equally important goals to solve
constrained many-objective optimization problems (CMaOPs). However, most existing …

Multi-objective optimization has been adopted in many engineering problems where a set of
requirements must be met to generate successful applications. Among them, there are the …

Among the constraint-handling techniques (CHTs) in constrained multiobjective
optimization, constrained dominance principle (CDP) is simple, flexible, nonparametric, and …

Multiobjective optimization evolutionary algorithms (MOEAs) have received significant
achievements in recent years. However, they encounter many difficulties in dealing with …

Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-
objective optimization problems (MaOPs). Generally, different solution-sorting methods …