Ming potential patterns from patient data are usually treated as a high‐dimensional data clustering problem. Evolutionary multiobjective clustering algorithms with feature selection (FS) are widely used to handle this problem. Among the existing algorithms, FS can be performed either before or during the clustering process. However, research on performing FS at both stages (hybrid FS), which can yield robust and credible clustering results, is still in its infancy. This paper introduces an improved high‐dimensional patient data clustering algorithm with hybrid FS called variable dimensional multiobjective lifetime constrained quantum PSO with reinforcement learning (VLQPSOR). VLQPSOR consists of two main independent stages. In the first stage, a dimensionality reduction ensemble strategy is developed before clustering to reduce the patient dataset’s dimensionality, resulting in subdatasets of varying dimensions. In the second stage, an improved multiobjective QPSO clustering algorithm is proposed to simultaneously conduct dimensionality reduction and clustering. To accomplish this, several strategies are employed. Firstly, the variable dimensional lifetime constrained particle learning strategy, the continuous‐to‐binary encoding transformation strategy, and multiple external archives elite learning strategy are introduced to further reduce the dimensionality of the subdatasets and mitigate the risk of QPSO getting trapped in local optima. Secondly, an improved reinforcement learning–based clustering method selection strategy is proposed to adaptively select the optimal classical clustering algorithm. Experimental results demonstrate that VLQPSOR outperforms five representative comparative algorithms across four validity indexes and clustering partitions for most patient datasets. Ablation experiments confirm the effectiveness of the proposed strategies in enhancing the performance of QPSO.

Abstract Efficient management of aircraft and crew recovery system is crucial for cost savings and improving the satisfaction, which are related to the airline's reputation. However, most existing work considers only one objective of minimizing costs or maximizing satisfaction. In this study, we propose a new integrated multi‐objective recovery system that takes both cost and satisfaction into account simultaneously. To better capture crew satisfaction in the event of airport closure, a bidding mechanism for early off‐duty task is designed. To overcome the experience‐dependent and labour‐consuming problems associated with current manual or mathematical recoveries, we develop an intelligent optimizer based on multi‐swarm and MOPSO frameworks, termed adaptive seeking and tracking multi‐objective particle swarm optimization algorithm (ASTMOPSO). Specifically, during the evolutionary process, the sub‐swarm size undergoes adaptive internal transfer while executing more efficient evolutionary strategies to approach the global Pareto front. Additionally, five ad‐hoc repair procedures are designed to ensure feasibility for our aircraft and crew recovery system. The ASTMOPSO is applied to real‐world instances from Shenzhen Airlines with different sizes. Experimental results demonstrate the statistical superiority of our method over other popular peer algorithms. And the infeasible solution repair procedures significantly improve the feasibility rate by at least 40%, particularly for large‐scale instances.

Integrated berth and quay crane allocation problem (BQCAP) are two essential seaside operational problems in container terminal scheduling. Most existing works consider only one objective on operation and partition of quay into berths of the same lengths. In this study, BQCAP is modeled in a multiobjective setting that aims to minimize total equipment used and overall operational time and the quay is partitioned into berths of different lengths, to make the model practical in the real-world and complex quay layout setting. To solve the new BQCAP efficiently, a multiobjective hydrologic cycle optimization algorithm is devised considering problem characteristics and historical Pareto-optimal solutions. Specifically, the quay crane of the large vessel in all Pareto-optimal solutions is rearranged to increase the chance of finding a good solution. Besides, worse solutions are probabilistic retained to maintain diversity. The proposed algorithm is applied to a real-world terminal scheduling problem with different sizes from a container terminal company. Experimental results show that our algorithm generally outperforms the other well-known peer algorithms and its variants on solving BQCAP, especially in finding the Pareto-optimal solutions range.