Types of training algorithms. by Caixia Yan (6647072)

Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20... by Ruyi Dong (9038174)

Subjects: “…dimensional benchmark functions…”

Proposed algorithm. by Keenjhar Ayoob (22339616)

CPU usage of benchmark function: (a) CPU Usage during the Execution of Benchmark Functions F21-F30, (b) CPU Usage during the Execution of Benchmark Functions F31-F40 and (c) CPU Us... by Ruyi Dong (9038174)

Subjects: “…dimensional benchmark functions…”

Iteration curve of benchmark function: (a) Convergence curves of the average best fitness for functions F21-F30, (b) Convergence curves of the average best fitness for functions F3... by Ruyi Dong (9038174)

Subjects: “…dimensional benchmark functions…”

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

A Scalable Route to First-Order Response Properties with Correlated Sampling Phaseless Auxiliary-Field Quantum Monte Carlo by Leon Otis (20898157)

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Flowchart for the Tabu Search Algorithm [43]. by Ferhat Yuna (21519851)

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The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. …”

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

“…The results demonstrate the effectiveness of the QUBO-MCLP algorithm workflow, with TOICCAP successfully converting slack variables in inequality-constrained functions for p>2, and LTOICCAP effectively reducing the number of linear and quadratic variables. …”

Constructing Accurate Potential Energy Surfaces with Limited High-Level Data Using Atom-Centered Potentials and Density Functional Theory by Mahsa Nazemi-Ashani (21876936)

“…We present a general method for developing a Δ-DFT-type approach that enables the generation of energies with complete basis set CCSD­(T)-level accuracy on the potential energy surfaces (PESs) of molecules of arbitrary size, while requiring only a minimal set (hundreds) of high-level wave function theory reference data points for fitting. …”

Results on the comparison of evolutionary and greedy algorithms with exact solution. by Alexander Andreev (16822333)

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Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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Water consumption due to average value and fuzzy logic algorithm. by Gourab Saha (8987405)

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CEC2017 Summary Analysis of Optimal Function Parameters. by Tengfei Ma (597633)

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ANOVA was performed to test the differences between the algorithms for each indicator. by Jihui Hu (19812736)

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Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Type-1 membership function for distance. by Seung-Min Ryu (21463891)

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Type-1 membership function for speed. by Seung-Min Ryu (21463891)

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