Loss function variation curve. by Chunhua Yang (346871)

“…<div><p>This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. …”

A comparison of whether to add a penalty. by Dawei Wang (471687)

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Optimization results of different algorithms. by Haijian Wang (746739)

“…<div><p>An Aquila optimizer-back propagation (AO-BP) neural network was used to establish an approximate model of the relationship between the design variables and the optimization objective to improve elevator block brake capabilities and achieve a lightweight brake design. Subsequently, the constraint conditions and objective functions were determined. …”

The structure of a speed reducer. by Yanzhao Gu (21192659)

“…The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. …”

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Model optimization flow by MFO algorithm. by Ruoqi Wang (14958062)

“…The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. …”

Benchmark test function results. by Ruyi Dong (9038174)

“…Moreover, the experimental results obtained by LLSKSO yielded smaller line densities and greater strengths compared to other algorithms. LLSKSO achieves theoretical optima in 16 out of 20 high-dimensional benchmark functions, with an average CPU runtime reduced by 30% compared to baseline methods. …”

Four, five, and ten-dimensional benchmark test-functions. by Salisu Mohammed (772274)

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Plots of convergence curves on 2-D benchmark functions. by Salisu Mohammed (772274)

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Plots of convergence curves on 30-D benchmark functions. by Salisu Mohammed (772274)

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Root mean square deviation (RMSD) of the backbone atoms of the protein as a function of time. by L. F. S. Bonet (11502082)

Recombinant mapping narrows region of interest to identify a loss-of-function allele of <i>mttp.</i> by McKenna Feltes (11242085)

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A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density. by Hassan Mehboob (8960273)

“…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…”

Time response of the en-CSA based condenser with respect to different algorithms of CSA, RUN, PDO and RIME. by Sarah A. Alzakari (19704611)

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Parameter sets of the chosen algorithms. by WanRu Zhao (18980374)

“…However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. …”

The flow chart of IERWHO algorithm. by WanRu Zhao (18980374)

“…However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. …”

The flow chart of WHO algorithm. by WanRu Zhao (18980374)

“…However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. …”

Comparative experimental data of loss functions. by Huiying Zhang (200681)

“…Additionally, three innovative loss functions—focalerDIoU, focalerGIOU and focalerShapeIoU are proposed to reduce losses during the training process. …”

Backbone dimension () as a function of relative age (). by Jonathan E Kings (20818367)

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

“…The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence. …”