Parameter settings for metaheuristic algorithms. by Junhao Wei (6816803)

“…In the experimental section, we validate the efficiency and superiority of LSWOA by comparing it with outstanding metaheuristic algorithms and excellent WOA variants. The experimental results show that LSWOA exhibits significant optimization performance on the benchmark functions with various dimensions. …”

Mean training time of different algorithms. by Wei Liu (20030)

“…The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Algorithm ranking under different dimensions. by Wei Liu (20030)

“…The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Disentangling the Contribution of Each Descriptive Characteristic of Every Single Mutation to Its Functional Effects by C. K. Sruthi (5391104)

Disentangling the Contribution of Each Descriptive Characteristic of Every Single Mutation to Its Functional Effects by C. K. Sruthi (5391104)

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

“…The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. …”

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

“…The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. …”

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

“…The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. …”

Compare algorithm parameter settings. by Yuqi Xiong (12343771)

“…Experimental validation shows that on 23 benchmark functions and the CEC2022 test suite, MESBOA significantly outperforms the original Secretary Bird Optimization Algorithm (SBOA) and other comparative algorithms (such as GWO, WOA, PSO, etc.) in terms of convergence speed, solution accuracy, and stability. …”

CEC2017 test function test results. by Tengfei Ma (597633)

“…The optimal individual’s position is updated by randomly selecting from these factors, enhancing the algorithm’s ability to attain the global optimum and increasing its overall robustness. …”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Generation of Non-diffraction Vortex Beam and Its Application in Digital Communication by Han Huang (1570573)

Similarity between the hierarchical community structure of structural and functional brain graphs. by Arian Ashourvan (6685232)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Description of unimodal benchmark functions. by Yi-Qiang Xia (20161326)

“…<div><p>This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. …”

Description of multimodal benchmark functions. by Yi-Qiang Xia (20161326)

“…<div><p>This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. …”

The control algorithm of MPC for SSI. by Ahmed Abdelaleem (19066115)

Statistical results of various algorithms. by ZeSheng Lin (20501356)

“…Secondly, the WOA’s position update formula was modified by incorporating inertia weight <i>ω</i> and enhancing convergence factor <i>α</i>, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)