The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

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DE algorithm flow. by Ling Zhao (111365)

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

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

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

Performance of traditional ML algorithms for optimal offloading scheme. by Shehr Bano (20421706)

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Performance of different ensemble algorithms for optimal partitioning policy. by Shehr Bano (20421706)

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Performance of traditional ML algorithms for optimal partitioning scheme. by Shehr Bano (20421706)

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Performance of different ensemble algorithms for optimal offloading policy. by Shehr Bano (20421706)

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MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”

Overview of the modeling approach using Marxan optimization algorithm. by Boris Salak (18340030)

SVR and MLPR algorithm flow optimized by GridSearchCV. by Jintao Li (448681)

Overall systemic risk in banking networks with different matching algorithms. by Bo Zhu (118038)

The hyperparameters selected for CLPREM, as well as other settings of the model, were determined based on experience and testing. by Xiaorui Wu (8322705)

Overview of mobile-edge-cloud server-based system. by Shehr Bano (20421706)

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Optimized network structure. by Menaa Nawaz (14304920)

Experiment 2: Evaluating modularity-based consensus clustering pipelines on synthetic networks based on clustered real-world networks. by Yasamin Tabatabaee (18095491)

Tuning parameters of PID based on DA, AOA, POA, and GRO. by H Abdelfattah (17854644)

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

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A schematic diagram of the distribution of base stations in a city shows that the distribution of base stations is not even, showing a trend of being dense inside and sparse outsid... by Xiaorui Wu (8322705)

Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. by Yasamin Tabatabaee (18095491)