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While surrogate model based techniques, including Kriging, are known for their high accuracy and efficiency, they typically employ a sequential learning strategy, which limits their potential for parallel computation. This paper introduces the Local Penalization Adaptive Learning (LP-AL) method, which facilitates parallel adaptive reliability analysis; LP-AL introduces a penalty function that emulates the process of sequential learning strategies, thereby achieving parallelization. The method also integrates a global error-based stopping criterion and a sample pool reduction strategy to enhance efficiency. We tested LP-AL with five commonly used learning functions across various engineering scenarios. 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