Persian Version of the Hyperacusis Questionnaire: Psychometric Evaluation and Prevalence Determination.

PURPOSE: Hyperacusis is an uncommon hearing disorder, known as increased sensitivity to daily surrounding sounds. This disorder can profoundly affect people's daily activities. The studies conducted about hyperacusis are very limited in Iran. The purposes of this study are the psychometric evaluation of the Persian version of the Hyperacusis Questionnaire (PHQ) and the investigation of its prevalence. METHOD: This cross-sectional study was accomplished on 203 young university students with normal hearing sensitivity. After translating the questionnaire, the psychometric properties of the PHQ were evaluated by content validity ratio (CVR) and index (CVI) and exploratory factor analysis (EFA). Students were evaluated by clinical audiology tests, the loudness discomfort level (LDL) measurement, and answering the PHQ. The research data collection was done from April to November 2022. Otoscopy, clinical and speech audiometry, and LDL were all executed, respectively. The participants directly answered the PHQ. All statistical analyses were performed using SPSS software Version 26. RESULTS: The PHQ demonstrated acceptable validity and reliability, Cronbach's alpha (.81), CVI (> 0.88), and CVR (> 0.98). EFA revealed four dimensions of the questionnaire. Of the participants, four (2%) were identified as suffering from hyperacusis. The PHQ showed the possibility of differences between genders. CONCLUSIONS: The psychometric evaluations of the PHQ were found to be acceptable and can be used in future studies. The prevalence of hyperacusis in our sample was 2%, and estimated to be higher in females. These findings suggest the need for further research on hyperacusis in Iranian population and specified studies to compare the two genders.

Squid Game Optimizer (SGO): a novel metaheuristic algorithm.

In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov-Smirnov, Mann-Whitney, and Kruskal-Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.

Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization.

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov-Smirnov, Wilcoxon, and Kruskal-Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems.

Shape and size optimization of truss structures by Chaos game optimization considering frequency constraints.

INTRODUCTION: An engineering system consists of properly established activities and put together to achieve a predefined goal. These activities include analysis, design, construction, research, and development. Designing and constructing structural systems, including buildings, bridges, highways, and other complex systems, have been developed over the centuries. However, the evolution of these systems has been prolonged because the overall process is very costly and time-consuming, requiring primary human and material resources to be utilized. One of the options for overcoming these shortcomings is the utilization of metaheuristic algorithms as recently developed intelligent techniques. These algorithms can be utilized as upper-level search techniques for optimization procedures to achieve better results. OBJECTIVES: Shape and size optimization of truss structures are considered in this paper utilizing the Chaos Game Optimization (CGO) as one of the recently developed metaheuristic algorithms. The principles of chaos theory and fractal configuration are considered inspirational concepts. METHODS: For the numerical purpose, the 10-bar, 37-bar, 52-bar, 72-bar, and 120-bar truss structures as four of the benchmark problems in this field are considered as design examples in which the frequency constraints are considered as limits that have to be dealt with during the optimization procedure. Multiple optimization runs are also conducted for having a comprehensive statistical analysis, while a comparative investigation is also conducted with other algorithms in the literature. RESULTS: Based on the results of the CGO and other approaches from the literature, the CGO can provide better and competitive results in dealing with the considered truss design problems. CONCLUSION: In summary, the CGO can provide better solutions in dealing with the considered real-size structural design problems with higher levels of complexity.