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Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm. However, this mutation strategy slows down convergence. It should achieve a global minimum by using 1000 × D, where D is the dimension, but due to exploration and exploitation balancing trade-offs, it can not achieve a global minimum within the range of 1000 × D in some of the objective functions. To overcome this issue, a new and enhanced mutation strategy and algorithm have been introduced in this paper, called DE/Neighbor/2, as well as an improved random neighbor-based differential evolution algorithm. The new DE/Neighbor/2 mutation strategy also uses neighbor information such as DE/Neighbor/1; however, in addition, we add weighted differences after various tests. The DE/Neighbor/2 and IRNDE algorithm has also been tested on the same 27 commonly used benchmark functions on which the DE/Neighbor/1 mutation strategy and RNDE were tested. Experimental results demonstrate that the DE/Neighbor/2 mutation strategy and IRNDE algorithm show overall better and faster convergence than the DE/Neighbor/1 mutation strategy and RNDE algorithm. The parametric significance test shows that there is a significance difference in the performance of RNDE and IRNDE algorithms at the 0.05 level of significance. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) stands out as a straightforward yet remarkably powerful evolutionary algorithm employed for real-world problem-solving purposes. In the DE algorithm, few parameters are used, and the population is evolved by applying various operations. It is difficult in evolutionary computation algorithms to maintain population diversity. The main issue is the sub-population of the DE algorithm that helps improve convergence speed and escape from the local optimum. Evolving sub-populations by maintaining diversity is an important issue in the literature that is considered in this research. A solution is proposed that uses sub-populations to promote greater diversity within the population and improve the algorithm performance. DE, heterogeneous distributed differential evolution (HDDE), multi-population ensemble differential evolution (MPEDE), and the proposed improved multi-population ensemble differential evolution (IMPEDE) are implemented using parameter settings; population sizes of 100 NP, 150 NP, and 200 NP; and dimensions of 10D, 30D, and 50D for performance comparison. Different combinations of mutations are used to generate the simulated results. The simulation results are generated using 1000, 3000, and 5000 iterations. Experimental outcomes show the superior results of the proposed IMPEDE over existing algorithms. The non-parametric significance Friedman test confirms that there is a significant difference in the performance of the proposed algorithm and other algorithms used in this study by considering a 0.05 level of significance using six benchmark functions. [ABSTRACT FROM AUTHOR]

Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance. [ABSTRACT FROM AUTHOR]

This study analyzes walking interaction to enhance the immersion and minimize virtual reality (VR) sickness of users by conducting experiments. In this study, the walking interaction is composed of three steps using input devices with a simple structure that can be easily used by anyone. The first step consists of a gamepad control method, which is the most popular but has low presence. The second step consists of a hand-based walking control interface, which is mainly used for interaction in VR applications. The last step consists of a march-in-place detection simulator that interacts with the legs-the key body parts for walking. Four experiments were conducted to determine the degree of direct expression of intention by users in the course of walking interactions that can improve immersion, presence, and prevent VR sickness in VR applications. With regard to the experiments in this study, survey experiments were conducted for general users using the Wilcoxon test, a presence questionnaire, and simulator sickness questionnaire (SSQ). In addition, the technical performance of the VR scenes used in the experiment was analyzed. The experimental results showed that higher immersion was achieved when interactions that felt closer to real walking were provided in VR. Furthermore, it was found that even control methods with a simple structure could be used for walking interactions with minimal VR sickness. Finally, a satisfactory presence was found in VR if the user was able interact using his or her own legs. [ABSTRACT FROM AUTHOR]

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance ( S d ). The S d is deduced from the concept of S-divergence (SD). Several characteristics of S d are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon's signed-rank test, Wilcoxon's rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape. [ABSTRACT FROM AUTHOR]

The sustainability of the supply chain is possible only if the profitability of all the tiers participating in that supply chain is guaranteed. The profitability of each of these tiers is ensured if information sharing as well as an effective and seamless coordination system are realized between the tiers. This process reduces the influence of an important risk factor known as the bullwhip effect. The purpose of the current study is to determine the necessary information sharing level to optimize the supply chain that has asymmetric flows of input and output values and to examine the effects of information sharing on the order fill rate (OFR) and total inventory cost (TIC) of the supply chain through analysis of variance (ANOVA) testing. In this work, the supply chain was optimized by using the particle swarm optimization (PSO) technique, with an objective function that assumes the maximization of OFR and minimization of TIC. The proposed method showed excellent results in comparing the mean, variance, and coefficient of variation. In addition, the method used the ANOVA test with a 5% significance level to verify the impact of the information sharing level. [ABSTRACT FROM AUTHOR]