Sohail Ahmad

This study aims to introduce a novel Non-Linear Diophantine Fuzzy Multi-Criterion Decision-Making Model for COVID-19 diagnosis and control. The study is organised into three sections to encourage individuals and to develop an appropriate strategy for emergency decision-making circumstances. First, we propose a generalizations of Pythagorean fuzzy sets, q-rung orthopair fuzzy sets, and linear Diophantine fuzzy set, called Non-linear Diophantine fuzzy set (Non-LDFS) and discussed their important properties. Moreover, mathematical criteria for Non-LDFSs are established based on certain operating laws. In the second part of the study, we propose a set of Non-LDF averaging and geometric aggregation operators for aggregating expert judgments based on TOPSIS techniques. In final part, the newly defined Non-LDF Topsis Method is used to solve a medical diagnosis challenge for the COVID-19 virus, and the findings are reported. To accomplish this, we developed an enhanced ”multi-criteria decision-making” (MCDM) technique that should be capable of evaluating and selecting the best alternative choice of COVID-19 based on five criteria. A comparative analysis is also performed for the novel Non-LDF Topsis, and the prospects of the designed research are addressed.

The costs of different fuels are increasing gradually, for operation of power production units. Thus new optimization techniques are needed to tackle the problems of Economic Load Dispatch (ELD). Metaheuristics are very helpful for policy makers in achieving the best results by minimizing the cost function. In this paper, we have updated the Grasshopper Optimization Algorithm (GOA) with a better initialization strategy to balance the search capability of GOA. The new algorithm is named as Improved Grasshopper Algorithm (IGOA). GOA is inspired by the swarms of grasshopper and mimics their biological behavior. Furthermore, IGOA is used to solve the ELD problems by tacking four case studies from literature. The objective in these problems is to find best decision variables for dispatching the available power with lowest cost, efficiency and reliability. To validate the efficiency of our proposed algorithm, we have tested it by solving 4 case studies of ELD with 1263MW, 600MW, 800MW and 2500MW demands respectively. IGOA is better in terms of convergence rate and quality of solutions obtained for the problems considered in literature for other metaheuristics.