Abstract
PURPOSE: Cognitive diagnostic tests (CDTs) assess cognitive skills at a more granular level, providing detailed insights into the mastery profile of test-takers. Traditional algorithms for constructing CDTs have partially addressed these challenges, focusing on a limited number of constraints. This paper intends to utilize a meta-heuristic algorithm to produce high-quality tests and handle more constraints simultaneously. METHODS: This paper presents a memetic ant colony optimization (MACO) algorithm for constructing CDTs while considering multiple constraints. The MACO method utilizes pheromone trails to represent successful test constructions from the past. Additionally, it innovatively integrates item quality and constraint adherence into heuristic information to manage multiple constraints simultaneously. The method evaluates the assembled tests based on the diagnosis index and constraint satisfaction. Another innovation of MACO is the incorporation of a local search strategy to further enhance diagnostic accuracy by partially optimizing item selection. The optimal local search parameter settings are explored through a parameter investigation. A series of simulation experiments validate the effectiveness of MACO under various conditions. RESULTS: The results demonstrate the great ability of meta-heuristic algorithms to handle multiple constraints and achieve high statistical performance. MACO exhibited superior performance in generating high-quality CDTs while meeting multiple constraints, particularly for mixed and low discrimination item banks. It achieved faster convergence than the ant colony optimization in most scenarios. CONCLUSIONS: MACO provides an effective solution for multi-constrained CDT construction, especially for shorter tests and item banks with mixed or lower discrimination. The experimental results also suggest that the suitability of different optimization approaches may depend on specific test conditions, such as the characteristics of the item bank and the length of the test.