Reward function related parameters. by Honglei Pang (22693724)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

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IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</p><p dir="ltr">The package includes:</p><ul><li>Scripts for first-order analysis, third-order modeling, optimization using a Physically Grounded Merit Function (PGMF), and RMS-based refinement.</li><li>A subfolder named <b>Images</b>, which stores the figures generated by six of the seven provided scripts.…”

Experiments comparing OMWOA with alternative competing algorithms on the IEEE CEC 2017 benchmark functions. by Kankan Tang (21474968)

Experiments comparing OMWOA with alternative competing algorithms at IEEE CEC 2022 benchmark functions. by Kankan Tang (21474968)

Scalability analysis on the IEEE CEC 2017 benchmark functions. by Kankan Tang (21474968)

Automatic translation error detection based on fuzzy decision tree algorithm. by Danqiangyu Zhou (22146196)

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Evolutionary trajectory of OMWOA on the IEEE CEC 2017 benchmark functions. by Kankan Tang (21474968)

DE algorithm flow. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

Pseudocode for the CORDIC algorithm to compute multiplication. by Mehdi Nadiri Andabili (22097275)

Search algorithm performance comparison. by Zhaohui Du (414105)

The flow of the genetic algorithm with the nested Frank-Wolfe algorithm. by Zhipeng Huang (1759759)

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Test results of different algorithms. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

Performance comparisons of OMWOA with state-of-the-art competitors on the IEEE CEC 2022 benchmark functions. by Kankan Tang (21474968)

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

Run times of two algorithms. by Gourab Saha (8987405)

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Selected optimal solutions from the QLNSGA-II algorithm. by Zhongfeng Li (3965060)

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Performance of different algorithms across various regression metrics and datasets. by Shikun Chen (14625352)