Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach

This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism Search Algorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are generated by mimicking different types and aspects of perfectionistic behavior. In order to have a complete perspective on the performance of PSA, the proposed algorithm is tested with various nonlinear optimization problems, through selection of 35 benchmark functions from the literature. The generated solutions for these problems, were also compared with 11 well-known meta-heuristics which had been applied to many complex and practical engineering optimization problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other well-known algorithms

Correlation between Screening estimation and noise measurement in Small Plants in Varamin city

Introduction: In Iran country, small enterprise with less than 10 workers comprise about %90 of all industries and 80% of working population. Noise, higher than the permissible level is among the hazardous agents, workers in these plants facing with. The aim of this study was to investigate the correlation between screening estimation and noise measurement in small plants. . Material and Method: This cross-sectional study was conducted in 51 plants with less than 5 workers. Noise screening was done using screening from. Next, noise level was measured in all the plants by TES-1358 sound level meter and regular grid method. Data were analyzed using chi-square test and linear regression in SPSS version 16. .Result: The mean (SD) scores of sound pressure level and screening form were 86.5 dBA (4.5) and 64.2 (9.4) in 51 understudy plants, respectively. According to the results of sound measurement, sound level in 34 plants (66.6%) exceeded the permissible level and in 17 plants (33.33%) was below the limit. The results of screening forms showed that 47 plants (92.16%) had permissible level of noise while noise in 4 (7.84%) was not in permissible level. Chi square test revealed no significant relationship between the results of the two methods, based on the permissible and impermissible limits (p-Value=0.288). Furthermore, according to the regression analysis, R2 was obtained 0.357.. Conclusion: No correlation was shown between the results of the two methods used. Thus, it is recommended to change the parameters used in the noise screening form for small plants, with less than 5 workers