Adaptive archive exploitation for Gaussian estimation of distribution algorithm

Abstract

The Gaussian Estimation of Distribution Algorithm (GEDA) is a fundamental evolutionary algorithm widely applied to continuous optimization problems but often encounters premature convergence. While external archives have been introduced to mitigate this issue, they frequently misuse historical information, leading to suboptimal results. To address this, we propose an Adaptive Archive Exploitation for GEDA (AAE-GEDA). AAE-GEDA incorporates two key mechanisms: adaptive selection of archive quantities (ASAQ) and angle skewness-landscape (ASL) eigenvalue adaptation. ASAQ selectively utilizes a subset of solutions from the archive to improve the accuracy of covariance estimation, preventing the algorithm from being misled by outdated or irrelevant information. ASL dynamically adjusts the search range, ensuring a balanced trade-off between exploration and exploitation. Experimental results on the IEEE CEC2014 and CEC2017 test suites demonstrate that AAE-GEDA consistently outperforms state-of-the-art evolutionary algorithms.