Artificial algae algorithm (AAA), which is one of the recently developed bio-inspired optimization algorithms, has been introduced by inspiration from living behaviors of microalgae. In AAA, the modification of the algal colonies, i.e. exploration and exploitation is provided with a helical movement. In this study, AAA was modified by implementing multi-light source movement and artificial algae algorithm with multi-light source (AAAML) version was established. In this new version, we propose the selection of a different light source for each dimension that is modified with the helical movement for stronger balance between exploration and exploitation. These light sources have been selected by tournament method and each light source are different from each other. This gives different solutions in the search space. The best of these three light sources provides orientation to the better region of search space. Furthermore, the diversity in the source space is obtained with the worst light source. In addition, the other light source improves the balance. To indicate the performance of AAA with new proposed operators (AAAML), experiments were performed on two different sets. Firstly, the performance of AAA and AAAML was evaluated on the IEEE-CEC'13 benchmark set. The second set was real-world optimization problems used in the IEEE-CEC'11. To verify the effectiveness and efficiency of the proposed algorithm, the results were compared with other state-of-the-art hybrid and modified algorithms. Experimental results showed that the multi-light source movement (MLS) increases the success of the AAA.
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http://dx.doi.org/10.1016/j.biosystems.2015.11.004 | DOI Listing |
PLoS One
October 2024
School of Computer Science and Engineeing, Northeastern University, Shenyang, Liaoning, P. R. China.
Cinematic Rendering (CR) employs physical models such as ray tracing and global illumination to simulate real-world light phenomena, producing high-quality images with rich details. In the medical field, CR can significantly aid doctors in accurate diagnosis and preoperative planning. However, doctors require efficient real-time rendering when using CR, which presents a challenge due to the substantial computing resources demanded by CR's ray tracing and global illumination models.
View Article and Find Full Text PDFSkin Res Technol
March 2024
Research and Development, Beiersdorf AG, Hamburg, Germany.
Background: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible.
View Article and Find Full Text PDFMicromachines (Basel)
December 2022
School of Mechanical and Electrical Engineering, Soochow University, No. 8, Jixue Road, Suzhou 215131, China.
Due to their fascinating solitary and collective behavior, photochemical microrobots have attracted extensive attention from researchers and have obtained a series of outstanding research progress in recent years. However, due to the limitation of using a single light source, the realization of reconfigurable and controllable motion behaviors of the photochemical microrobot is still facing a series of challenges. To release these restrictions, we reported a multi-light-field-coupling-based method for driving the photochemical microrobot or its swarm in a regulatable manner.
View Article and Find Full Text PDFSci Rep
December 2022
Key Laboratory of Deep Coal Mining of the Ministry of Education, School of Mines, China University of Mining and Technology, Xuzhou, 221116, China.
Using image recognition technology to realize coal gangue recognition is one of the development directions of intelligent fully mechanized caving mining. Aiming at the problem of low accuracy of coal gangue recognition in fully mechanized caving mining, the extraction method of Coal and gangue images features is proposed, and the corresponding coal gangue recognition model is constructed. The illuminance value is an important factor affecting the imaging quality.
View Article and Find Full Text PDFJ Pharm Sci
January 2020
Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 2, 76131 Karlsruhe, Germany. Electronic address:
Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious time-consuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images.
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