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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Graphic representation of the CAP A events identified by the algorithm (blue line) and by the scorer (red line). by Maria Paola Tramonti Fantozzi (11785619)

Algorithm parameters. by Ruyi Dong (9038174)

“…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”

Complex traffic line and track point diagram. by Mingkang Sun (6498791)

Complex traffic line and track point diagram. by Mingkang Sun (6498791)

Service Coverage Optimization for Facility Location: Considering Line-Of-Sight Coverage in Continuous Demand Space by XIAOYA MA (12164486)

Steady-State Simulation Performance at 1000 <i>W</i>/<i>m</i>² (a) Conventional P&O Algorithm, (b) Proposed SF Algorithm. by Rana Ahmed (4923127)

Assessing the overall accuracy of the solutions obtained by the algorithms. by Nasiru Salihu (14803855)

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

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Transient Simulation Performance at 800<i>W</i>/<i>m</i>² and 400<i>W</i>/<i>m</i>² (a) Conventional P&O Algorithm, (b) Proposed SF Algorithm. by Rana Ahmed (4923127)

Automated Discovery and Optimization of 3D Topological Photonic Crystals by Samuel Kim (1682104)

“…They are typically designed by hand based on the careful analysis of their bands and mode profiles, but recent theoretical advances have revealed new and powerful insights into the connection between band symmetry, connectivity, and topology. Here we propose a combined global and local optimization framework that integrates a flexible symmetry-constrained level-set parametrization with standard gradient-free optimization algorithms to optimize topological photonic crystals, a problem setting where the objective function may be highly nonconvex and noncontinuous. …”

An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

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Medium-scale dataset comparative analysis using fitness and cost values for population size 50 and 100. by Olaide N. Oyelade (14047002)

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Large-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

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Datasets and their properties. by Olaide N. Oyelade (14047002)

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An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

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Small-scale dataset comparative analysis using fitness and cost values for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

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Medium-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

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Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

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Large-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

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