Computerized adaptive testing (CAT) is an efficient testing mode, which allows each examinee to answer appropriate items according his or her latent trait level. The implementation of CAT requires a large-scale item pool, and item pool needs to be frequently replenished with new items to ensure test validity and security. Online calibration is a technique to calibrate the parameters of new items in CAT, which seeds new items in the process of answering operational items, and estimates the parameters of new items through the response data of examinees on new items. The most popular estimation methods include one EM cycle method (OEM) and multiple EM cycle method (MEM) under dichotomous item response theory models. This paper extends OEM and MEM to the graded response model (GRM), a popular model for polytomous data with ordered categories. Two simulation studies were carried out to explore online calibration under a variety of conditions, including calibration design, initial item parameter calculation methods, calibration methods, calibration sample size and the number of categories. Results show that the calibration accuracy of new items were acceptable, and which were affected by the interaction of some factors, therefore some conclusions were given.

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An improved optimization algorithm, namely, multi-strategy-sparrow search algorithm (MSSSA), is proposed to solve highly non-linear optimization problems. In MSSSA, a circle map is utilized to improve the quality of the population. Moreover, the adaptive survival escape strategy (ASES) is proposed to enhance the survival ability of sparrows. In the producer stage, the craziness factor integrated with ASES is introduced to enhance the search accuracy and survival ability. In the scout stage, the ASES facilitates sparrows successful escape from danger. Besides, opposition-based learning or Gaussian–Chachy variation helps optimal individuals escape from local solutions. The performance of the MSSSA is investigated on the well-known 23 basic functions and CEC2014 test suite. Furthermore, the MSSSA is applied to optimize the real-life engineering optimization problems. The results show that the algorithm presents excellent feasibility and practicality compared with other state-of-the-art optimization algorithms.

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