%0 Dataset
%T PTLSO Large-Scale Optimization Simulation Dataset
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/e1d1d1e3-03eb-47a7-abf4-005b5c0c0d6a
%W NCDC
%A yang qiang
%K Large-scale optimization;Probabilistic updating;Tournament learning;Particle swarm optimization;High-dimensional optimization
%X All evaluation data in this experiment were automatically generated by numerical simulation programs. Each data file is in plain text format (.txt) and stores numerical values in a single column. Files named with the pattern Fitness_result_for_X.txt record the optimal objective function values obtained by the algorithm over 30 independent runs on the corresponding 20 test problems. Multiple high‑dimensional optimization test samples covering dimensionalities of 500, 1000, 1500, and 2000 were constructed, which are divided into four categories according to problem structures: fully separable, partially separable, fully non‑separable, and overlapping. The generated data are used for three major experimental scenarios: horizontal performance comparison among algorithms, scalability testing with respect to dimensionality, and ablation study of the core algorithm modules. For each set of samples, after 30 independent simulation iterations, multi‑dimensional statistical data are output, including mean, median, standard deviation, p‑values from significance tests, overall ranking, and convergence trajectories, which completely quantify the algorithm's solution accuracy, convergence speed, and optimization stability.