Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm

Quantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs provide better balance between exploration and exploitation as compared to the conventional evolutionary algorithms. The population in QEA is evolved by variation operators, which move the Q-bit towards an attractor. A modification for improving the performance of QEA was proposed by changing the selection of attractors, namely, versatile QEA. The improvement attained by versatile QEA over QEA indicates the impact of population structure on the performance of QEA and motivates further investigation into employing fine-grained model. The QEA with fine-grained population model (FQEA) is similar to QEA with the exception that every individual is located in a unique position on a two-dimensional toroidal grid and has four neighbors amongst which it selects its attractor. Further, FQEA does not use migrations, which is employed by QEAs. This paper empirically investigates the effect of the three different population structures on the performance of QEA by solving well-known discrete benchmark optimization problems

Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Classification problems are increasing in various important applications such as text categorization, images, medical imaging diagnosis and bimolecular analysis etc. due to large amount of attribute set. Feature extraction methods in case of large dataset play an important role to reduce the irrelevant feature and thereby increases the performance of classifier algorithm. There exist various methods based on machine learning for text and image classification. These approaches are utilized for dimensionality reduction which aims to filter less informative and outlier data. Therefore, these approaches provide compact representation and computationally better tractable accuracy. At the same time, these methods can be challenging if the search space is doubled multiple time. To optimize such challenges, a hybrid approach is suggested in this paper. The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. The results are verified using text and image data which reflects improved accuracy compared with other conventional techniques. A 25 benchmark datasets (UCI) from different domains are considered to test the proposed algorithms.  A comparative study between proposed hybrid classification algorithms are presented in this work. Finally, the experimental result shows that the differential evolution with NB classifier outperforms and produces better estimation of probability terms. The proposed technique in terms of computational time is also feasible

Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach

The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks