Purpose: Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.Design/methodology/approach: A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM.Findings: This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well.Originality/value: The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.

The Differential Evolution (DE) algorithm is an efficient algorithm with strong search capability. This algorithm is easy to implement and used for solving large-scale optimization problems. However, DE also has drawbacks of insufficient diversity in the later search stage, while solving optimization problems, slow convergence speed, and a high search stagnation possibility. Quantum Entanglement inspired Differential Evolution algorithm (QE-DE) has been proposed in this paper to solve the drawbacks of DE. For solving high-dependency optimization problems, QE-DE integrates entangled states in its Qubits (or quantum bit). The optimization process is further accelerated by utilizing the quantum local search. The proposed QE-DE algorithm is evaluated on the IEEE Congress of Evolutionary Computing (CEC 2017) benchmark set for 10 and 30-dimensions. QE-DE is also compared with existing variants of DE and some other popular algorithms. QE-DE outperforms the performance comparison results.