Search

The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches. [ABSTRACT FROM AUTHOR]

Exploring an optimal combination of features from high-dimensional text vectors is an essential step for classification on Scientific and Technical Service (STS) resources. However, existing metaheuristic feature selection methods exhibit deteriorated performance and increased computational costs when dealing with high-dimensional datasets. Based on these issues, a novel Enhanced Binary Black Hole (EBBH) algorithm is investigated to improve the performance of mining the optimal subset of high-dimensional attributes in STS resources text. In the proposed EBBH, all operators employ a novel binary encoding framework to reduce the number of features, which provides a more efficient way to search the solution space. As a pre-processing step, prime feature attention assigns different selection weights based on the variable importance ranking from random forests, ensuring a high-quality initial population is generated. Moreover, we incorporate an adaptive enhancement strategy to maintain the balance between exploitation and exploration in the search process. The proposed EBBH is assessed on eight high-dimensional benchmark datasets and subsequently demonstrated successful application to five STS resource datasets. Comparative experiments with 12 state-of-the-art algorithms for high-dimensional problems revealed the superiority of EBBH in terms of fitness, classification accuracy, and selected features. [ABSTRACT FROM AUTHOR]