Captured retinal images usually have quality issues, manifested as containing multiple distortions (e.g., low light and blurring). Low-quality images bring a challenge to the screening and diagnosis of ophthalmic diseases. Existing image enhancement methods typically neglect the analysis of distortions and require high-quality reference images for model learning, making them unsuitable for clinical applications. In this paper, we propose a Distortion-Aware Network (DANet) for retinal image enhancement in a zero-reference way. DANet consists of three parallel branches by incorporating atmospheric scattering theory, which decomposes the low-quality image into a clean image, a transmission map, and an atmospheric light map. The upper branch utilizes a dark channel prior module to estimate the atmospheric light map, and the middle branch uses a transmission map generation module to estimate the transmission map. In contrast, the lower branch uses a deblurring module and a low-light enhancement module to obtain a deblurred image and an illumination-enhanced image and fuses these two images using a fusion block to generate the final enhanced image. Taking into account the limited publicly available datasets, we curate two datasets for the retinal image enhancement task. Experimental results show that our DANet can greatly improve the visual quality of the image with good interpretability, achieving superior performance over seven state-of-the-art methods.

To address the challenge of parameter adjustment in complex environments, this paper introduces a transfer learning-based parameter control framework via deep reinforcement learning for multiobjective evolutionary algorithms (MOEAs). To avoid the requirement for accurate Pareto front information, this framework is proposed with comprehensive global-state information, including basic problem features, the relative position of individuals, the distribution of fitness value, and the grid-IGD. Building on this framework, four reinforced multiobjective evolutionary algorithms (r-MOEAs) are proposed and tested on four DTLZ benchmarks and eight WFG benchmarks. The results of the comparative analyses reveal that compared with the original MOEAs, the four r-MOEAs exhibit faster convergence and stronger robustness. It is also confirmed that our proposed parameter control framework has the capability to learn knowledge from different experiences and improve the performance of MOEAs.

This study focuses on the challenges of aviation maintenance technician (AMT) scheduling and constructs a model based on personnel satisfaction and the parallel execution of aircraft maintenance tasks. To obtain the scheduling scheme from the constructed NP‐hard model, an interactive multi swarm bacterial foraging optimization (IMSBFO) algorithm is proposed using multi‐swarm coevolu tion, structural recombination, and three informa tion interactive mechanisms among individuals. Moreover, considering the distributed feature of the AMT scheduling problem, a specific mechanism is designed to convert continuous solution to a binary AMT scheduling scheme. Finally, a series of com parative experiments highlight the efficiency and superiority of our proposed IMSBFO algorithm, and the optimal scheduling scheme owns the delicate balance between the work and rest time.

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.