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Multi-strategy adaptive differential evolution with dimension-based pre-selection and state-based population adjustment for numerical optimization
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DOI：10.1016/j.swevo.2026.102518.png)
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Abstract
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In this study, we propose an innovative dimension-based multi-strategy adaptive differential evolution algorithm, termed DMDE, by leveraging the dimensional information and search state of population to determine the offspring among various candidate solutions and dynamically adjust the population reduction module, respectively. First, to avoid the estimation bias caused by Euclidean distance in the process of selecting offspring in the composite search strategies, a new dimension-based pre-selection method is designed. Herein, the neighbor information of candidate solution based on each dimension is taken into account, and the median of their fitness values is leveraged to decide the offspring for target individual due to its strong tolerance for outliers. Meanwhile, a state-based adaptive population adjustment mechanism is further developed to dynamically regulate the algorithm’s search capability. In this mechanism, we record the fitness improvements of superior and inferior individuals to gauge the difficulty of optimization process, and we employ two distinct population reduction modules tailored to different optimization scenarios. Through these approaches, the proposed algorithm can more accurately identify suitable candidate solution as offspring and achieve a better trade-off between exploration and exploitation. Finally, the performance of DMDE is validated by benchmarking it against 21 renowned or up-to-date methods on CEC’2017 test suite within different dimensions and applying to two distinct practical application scenarios. Numerical outcomes reveal that the new algorithm exhibits significantly superior performance compared to its counterparts.
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