Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Global dynamics of a dengue epidemic mathematical model

Abstract: The paper investigates the global stability of a dengue epidemic model with saturation and bilinear incidence. The constant human recruitment rate and exponential natural death, as well as vector population with asymptotically constant population, are incorporated into the model. The model exhibits two equilibria, namely, the disease-free equilibrium and the endemic equilibrium. The stability of these two equilibria is controlled by the threshold number R 0 . It is shown that if R 0 is less than one, the disease-free equilibrium is globally asymptotically stable and in such a case the endemic equilibrium does not exist; if R 0 is greater than one, then the disease persists and the unique endemic equilibrium is globally asymptotically stable.

An enhanced moth flame optimization

Abstract: Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transverse orientation of moths in nature. The transverse orientation is a special kind of navigation method, which demonstrates the movement of moths toward moon in a straight path. This algorithm has been successfully applied on various optimization problems. But, MFO suffers from the problem of poor exploration. So, in order to enhance the performance of MFO, some modifications are proposed. A Cauchy distribution function is added to enhance the exploration capability, influence of best flame has been added to improve the exploitation and adaptive step size and division of iterations is followed to maintain a balance between the exploration and exploitation. The proposed algorithm has been named as enhanced moth flame optimization (E-MFO) and to validate the applicability of E-MFO, and it has been applied to twenty benchmark functions. Also, comprehensive comparison of E-MFO with other meta-heuristic algorithms like bat algorithm, bat flower pollination, differential evolution, firefly algorithm, genetic algorithm, particle swarm optimization and flower pollination algorithm has been done. Further, the effect of population and dimension size on the performance of MFO and E-MFO has been discussed. The experimental analysis shows the superior performance of E-MFO over other algorithms in terms of convergence rate and solution quality. Also, statistical testing of E-MFO has been done to prove its significance.

Correction to: Some Aggregation Operators for Linguistic Intuitionistic Fuzzy Set and its Application to Group Decision-Making Process Using the Set Pair Analysis

Abstract: Linguistic intuitionistic fuzzy set (LIFS) is the better way to deal with the uncertain and imprecise information in group decision-making problems. On the other hand, the set pair analysis (SPA) theory provides a quantitative analysis to integrate the certainty and uncertainties as a combined system by defining the connection number corresponding to it. In the present paper, we have enhanced the LIFS with the SPA theory and hence defined the linguistic connection number (LCN) and its various operational laws. Based on it, we have developed various aggregation operators, namely LCN weighted geometric, LCN ordered weighted geometric, and LCN hybrid geometric operators with LIFS environment. Also, the shortcoming of the existing operators under LIFS environment has been highlighted and overcomes by the proposed operators. Few properties of these operators have been also investigated. Further, a group decision-making approach has been presented, based on these operators, which has been illustrated by a numerical example to show the effectiveness and validity of the proposed approach.