Photoacoustic imaging (PAI) is an emerging hybrid imaging modality that combines high-contrast optical imaging with high-spatial-resolution ultrasound imaging. PAI can provide a high spatial resolution and significant imaging depth by utilizing the distinctive spectroscopic characteristics of tissue, which gives it a wide variety of applications in biomedicine and preclinical research. In addition, it is non-ionizing and non-invasive, and photoacoustic (PA) signals are generated by a short-pulse laser under thermal expansion. In this study, we describe the basic principles of PAI, recent advances in research in human and animal tissues, and future perspectives.

Ship replenishment path planning has always been a critical concern for researchers in the field of security. This study proposes a modified whale optimization algorithm (MWOA) to address single-task ship replenishment path planning problems. To ensure high-quality initial solutions and maintain population diversity, a hybrid approach combining the nearest neighbor search with random search is employed for initial population generation. Additionally, crossover operations and destroy and repair operators are integrated to update the whale’s position, significantly enhancing the algorithm’s search efficiency and optimization performance. Furthermore, variable neighborhood search is utilized for local optimization to refine the solutions. The proposed MWOA has been tested against several algorithms, including the original whale optimization algorithm, genetic algorithm, ant colony optimization, hybrid particle swarm optimization, and simulated annealing, using traveling salesman problems as benchmarks. Results demonstrate that MWOA outperforms these algorithms in both solution quality and stability. Moreover, when applied to ship replenishment path planning problems of varying scales, MWOA consistently achieves superior performance compared to the other algorithms. The proposed algorithm demonstrates high adaptability in addressing diverse ship replenishment path planning problems, delivering efficient, high-quality, and reliable solutions.

Ship replenishment path planning is a critical problem in the field of maritime logistics. This study proposes a novel synergistic hybrid optimization algorithm (SHOA) that effectively integrates ant colony optimization (ACO), the Clarke–Wright algorithm (CW), and the genetic algorithm (GA) to solve the capacitated multi-ship replenishment path planning problem (CMSRPPP). The proposed methodology employs a three-stage optimization framework: (1) initial path generation via parallel execution of the CW and ACO; (2) population initialization for the GA by strategically combining optimal solutions from ACO and the CW with randomized solutions; (3) iterative refinement using an enhanced GA featuring an embedded evolutionary reversal operation for local intensification. To evaluate performance, the SHOA is benchmarked against ACO, the GA, the particle swarm optimization algorithm, and the simulated annealing algorithm for the capacitated vehicle routing problem. Finally, the SHOA is applied to diverse CMSRPPP instances, demonstrating high adaptability, robust planning capabilities, and promising practical potential.

Due to the high density of warehouse distribution, port cargo is highly susceptible to fire, humidity, and various natural disasters. When these disasters occur, they can result in severe cargo losses. Meanwhile, transportation cost control has long been a prominent research focus in port logistics due to the enormous throughput. To address these problems, this paper proposes a two-stage allocation–transportation framework based on improved holistic swarm optimization. In the allocation stage, this paper designs a warehouse zoning strategy. Based on the distance criterion, a clustering method is employed to group spatially proximate warehouses into the same zone, and distribute the same cargo across different zones. In this way, the same cargo could be prevented from being completely destroyed in a disaster. In the transportation stage, this paper designs an improved holistic swarm optimization algorithm to plan the routes of cargo transportation. In this method, this paper integrates the greedy search algorithm, ant colony optimization, and adaptive swap/reversal operations with the holistic swarm optimization algorithm, which could enhance global search capability and significantly reduce the length of transportation routes. To validate the effectiveness of the proposed algorithm, experiments are conducted on both instances of varying scales and CVRPLIB benchmark instances, and the results are compared with several baseline methods. Specifically, compared with the best-performing baseline ACO, IHSO reduces the optimal route length by up to 2.08% on the self-generated instances and by 5.24% on the CVRPLIB benchmark instances. Moreover, all improvements are statistically significant under the Wilcoxon signed-rank test (p < 0.05), confirming that the advantage of IHSO is systematic rather than incidental. The proposed algorithm demonstrates high adaptability in solving port cargo path planning problems, delivering high-quality solutions.