The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …

This paper investigates a multi-objective job shop scheduling problem with manual loading
and unloading tasks (MOJSSPLU) aiming to minimize makespan and total workload …

Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …

Quality and safety are important and are the leading concerns in a construction project.
Planning a project without properly incorporating these two performance parameters …

In recent years, the number of stakeholders of construction projects has significantly
increased; this has required the simultaneous achievement of competing objectives, such as …

Decomposition-based evolutionary algorithms using predefined reference points have
shown good performance in many-objective optimization. Unfortunately, almost all …

It is critical to maintain significant convergence and diversity in many-objective optimization
problems (MaOPs) for the performance of many-objective evolutionary algorithms …

Among many-objective optimization problems (MaOPs), the proportion of nondominated
solutions is too large to distinguish among different solutions, which is a great obstacle in the …

In recent years, a number of multi-objective immune algorithms (MOIAs) have been
proposed as inspired by the information processing in biologic immune system. Since most …

Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-
supervised node classification. However, most existing GNNs suffer from the nonrobustness …