Objective Particulate matter (PM) as an environmental pollutant is suspected to be associated with autism spectrum disorder (ASD). The aim of this study was to assess whether exposures to PM2.5 during the first three years of life in relation to the risk and degree of the severity of ASD. Methods A total of two hundred and ninety‐seven 3–6 years old Chinese children (99 confirmed autism cases and 198 their age‐gender matched control subjects) were included. Children's exposures to PM2.5 (particulate matter with aerodynamic diameter <2.5 μm) during the first three years after birth were estimated. Logistic regression analysis was used to examine the PM2.5‐ASD association. Results The mean levels of PM2.5 exposures in ASD and typical developmental children during the first three years of life were 89.8[standard deviations (SD): 6.1] μg/m3 and 87.3(6.6) μg/m3, respectively (p = 0.002). A statistically significant positive correlation was found between the serum levels of PM2.5 and the Childhood Autism Rating Scale (CARS) score indicating severity of autism (r = 0.259; p = 0.010). Based on the receiver operating characteristic (ROC) curve, the optimal cutoff value of PM2.5 levels as an indicator for auxiliary diagnosis of ASD was projected to be 89.5ug/m3, which yielded a sensitivity of 65.4% and a specificity of 63.2%, with the area under the curve at 0.61 (95% confidence intervals [CIs], 0.54−0.68; P < 0.001). Multivariate analysis models were used to assess ASD risk according to PM2.5 quartiles (the lowest quartile [Q1] as the reference), with the adjusted odds ratios (ORs) (95% CIs) were recorded. As shown in the Table 2, the 3rd and 4th quartile of PM2.5 were compared against the Q1, and the risks were increased by 103% (OR = 2.03; 95%CI: 1.13–5.54; p = 0.015) and 311% (4.15; 2.04–9.45; p = 0.002), respectively. Conclusions To conclude, the evidence from this study allowed us to conclude that there was an association between PM2.5 exposure and ASD risk and severity.

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MSOPS-II is an excellent evolutionary optimizer capable of true many-objective optimization and free from the objective dimension barrier. It provides an automatic target vector generation scheme and a new fitness assignment method that including constraint information. This paper proposes MSOPS-IIA, which adds several modifications to the original MSOPS-II and is an accelerated version of MSOPS-II. These modifications include the evolutionary velocity deriving from the elites and a gradually decreasing mutation standard deviation. Experimental results show that MSOPS-IIA not only inherits the fine quality of MSOPS-II but also accelerates the convergence speed of MSOPS-II.