2026-10-15T21:42:24Z https://data.mendeley.com/oai

oai:data.mendeley.com/frbhptb5tj.1 2026-09-11T00:08:25Z

Ceballos, Johan Data and Python code for multiobjective spherical sensor design using angular diversity Mendeley Data This deposit contains Python code and computational results for arranging twelve sensors on a unit sphere. The study considers angular collision, geometric coverage and the loss of angular diversity when one sensor is removed. An NSGA-II-style variant with uniform parent selection is compared with random search over eight seeds, using 2,440 objective evaluations per method and run. The files include all evaluated configurations, objective values, nondominated archives and the selected compromise design. Additional analyses calculate exact dominated fractions within stated reference boxes and describe variation between paired runs. One Fibonacci layout and eight greedy maximin layouts provide geometric comparisons, with coverage checked on denser grids. The results show a trade-off between diversity and coverage; they do not establish better sensing accuracy or general superiority of the evolutionary method. All configurations and observations were generated computationally. Additional checks include an exact sign test, recomputation of 48 archived objective vectors, and fixed-layout angular resolution sensitivity at 18, 36 and 72 bins. These checks do not constitute independent physical validation or reoptimisation at alternative resolutions. Global Optimization Multi-Objective Optimization Popayán-Hernández, Juan Guillermo Saurith, Juan Dataset doi:10.17632/frbhptb5tj.1 oai:data.mendeley.com/frbhptb5tj.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/frbhptb5tj 2026-09-11T00:08:25Z