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This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.

Tropospheric ozone (O3) concentration continued to increase over past years, thereby becoming a growing environmental concern in China. Most studies have focused on the analysis of daily maximum 8‐hr O3 concentration, while there is still a dearth of investigations of nocturnal O3. Here, by analyzing the data of 1,313 sites from the China National Environmental Monitoring Center, we show a remarkable increase in the nocturnal O3 concentration during summertime of 2015–2019 in most regions of China, revealing an aggravation of nocturnal O3 pollution. Combining with a GeoDetector model and statistical analysis, we clarify that the aggravation of nocturnal O3 pollution is mainly caused by reduction in both ambient nitrogen dioxide (NO2) concentration and wet scavenging in recent years. We further reveal that the increasing O3 may have enhanced the nocturnal particulate nitrate (NO3− ${{\mathrm{N}\mathrm{O}}_{3}}^{-}$) formation through N2O5 heterogeneous hydrolysis, and thereby driving the variation and long‐term trend of nocturnal NO3− ${{\mathrm{N}\mathrm{O}}_{3}}^{-}$.