Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Handling conflicting objectives and finding multiple Pareto optimal solutions are two
challenging issues in solving multiobjective optimization problems (MOPs). Inspired by the …

Currently, one of the most popular research topics is the development of a new meta-
heuristic algorithm for solving multi-objective optimization problems. However, few of the …

Conventional multiobjective optimization algorithms (MOEAs) with or without preferences
are successful in solving multi-and many-objective optimization problems. However, a …

It is known that many-objective optimization problems (MaOPs) often face the difficulty of
maintaining good diversity and convergence in the search process due to the high …

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing
many conflicting objectives simultaneously. Decomposition-based many-objective …

In preference-based multi-objective optimization, knee solutions are termed as the implicit
preferred promising solution, particularly when users have trouble in articulating any …

The tourist trip design problem (TTDP) helps the trip planners, such as tourists, tour
companies, and government agencies, automate their trip planning. TTDP solver chooses …

In classification tasks, feature selection (FS) can reduce the data dimensionality and may
also improve classification accuracy, both of which are commonly treated as the two …