Annals of Optimization Theory and Practice
Volume:3 Issue: 3, Autumn 2020

This study focuses on developing an effective channel estimation approach using swarm Intelligence. The Orthogonal Frequency Division Multiplexing ( OFDM) is a modulation technique used to counter transmission channel frequency selection to reach high data rate without disruption. The theory of OFDM is to gain prominence in the field of wireless communication. OFDM is combined with the transmitter and receiver antenna to amplify the variety gain and improve system capacity on selective time and frequency channels, resulting in a Multiple Input Multiple Output ( MIMO) pattern. The most commonly used channel estimation techniques are the Least Square (LS) approaches and Minimum Mean Square Error (MMSE) approaches. In LS, the estimation1process is simple but the problem is that the square error has a high mean. The MMSE is better in Low SNR than in LS, but its main problem is its high computational complexity. A unique method is proposed in this research study that combines LS and MMSE to overcome the aforementioned problems. Upgraded PSO is introduced in this study to select the best channel. This proposed approach is also more efficient and requires less time compared to other techniques to estimate the best channel.

The paper aims to develop an idea of some inducing operators, namely induced intuitionistic fuzzy Einstein hybrid averaging operator, induced intuitionistic fuzzy Einstein hybrid geometric operator, induced generalized intuitionistic fuzzy Einstein hybrid averaging operator and induced generalized intuitionistic fuzzy Einstein hybrid geometric operator along with their wanted structure properties such as, monotonicity, idempotency and boundedness. The proposed operators are competent and able to reflect the complex attitudinal character of the decision maker by using order inducing variables and deliver more information to experts for decision-making. To show the legitimacy, practicality and effectiveness of the new operators, the proposed operators have been applied to decision making problems

Correlation measure is a vital measuring operator with vast applications in decision-making. On the other hand, intuitionistic fuzzy set (IFS) is very resourceful in soft computing to tackle embedded fuzziness in decision-making. The extension of correlation measure to intuitionistic fuzzy settinghas proven to be useful in multi-criteria decision-making (MCDM). This paper introduces a new intuitionistic fuzzy correlation measure encapsulates in an algorithm by taking into account the complete parameters of IFSs. This new computing technique evaluates the strength of relationship and it is defined within the codomain of IFS. The proposed technique is demonstrated with some theoretical results, and numerically authenticated to be superior in terms of performance index in contrast to some existing correlation measures. We demonstrate the application of the new correlation measure coded with JAVA programming language in medical diagnosis to enhance efficiency since diagnosis is a delicate medical-decision-making exercise.

Multi-attribute decision making (MADM) is a hot research area in fuzzy mathematics and to deal with that, the averaging and geometric aggregation operators (AOs) are the widely used tools. The aim of this manuscript is to propose the notion of averaging and geometric AOs in the environment of T-spherical fuzzy sets (TSFSs). TSFS enables the selection of grades of memberships from considerably a larger domain and hence overcome the drawbacks of the existing fuzzy frameworks. In this paper, we develop some novel operations for TSFSs including algebraic sum, product etc. Based on new operations some averaging AOs including T-spherical fuzzy weighted averaging (TSFWA) and T-spherical fuzzy weighted geometric (TSFWG) operators are developed. The monotonicity, idempotency and boundedness of the defined operators are investigated, and their fitness is validated using induction method. With the help of an illustrative example, the problem of policy decision making using a MADM algorithm is solved. The new proposed work and the existing literature is compared numerically and the advantages of the TSFWA and TSFWG operators are investigated over existing work.

Stochastic programming is often used to solve optimization problems where parameters are uncertain. In this article, we have proposed a mathematical model for a three-stage transportation problem, where the parameters, namely transport costs, demand, unload capacity and external purchasing costs are uncertain. In order to remove the uncertainty, we have proposed a new transformation technique to reformulate the uncertain model deterministically with the help of Essen inequality. The obtained equivalent deterministic model is nonlinear. Furthermore, we have provided a theorem to ensure that the deterministic model gives a feasible solution. Finally, a numerical example, following uniform random variables, is presented to illustrate the model and methodology.

The main objective of this paper is to introduce the idea of picture fuzzy labelling of graphs and the concepts of strong arc, partial cut node, bridge of picture fuzzy labelling graphs, picture fuzzy labelling tree and cycle along with their properties and results. In addition, an application of the picture fuzzy graph labelling model for the human circulatory system has been discussed.

In this paper we introduce some new separation axioms by utilizing the notions of alphaomega-p-open sets and alphaomega-pre closure operator and the implication between the existing spaces are provided. Also as an application, we study some continuous functions and graph functions using this separation axioms. Basic theorems and properties are also investigated.

Now a day’s uncertainty is a common thing in science and technology. It is undesirable also. Based on alternative view, it should be avoided by all possible means. Based on modern view uncertainty is considered essential to science and technology, it is not only the unavoidable plague but also it has impacted a great utility. Fuzzy set theory mainly developed based on inexactness, vagueness, relativity etc. fuzzy set may be used in mathematical modelling in every scientific discipline. It can also use for improving the generality of analytical solution. It has many uses in various streams like -operation research, control theory differential equations, fuzzy system reliability, optimization and management sciences etc. In this paper we first describe Trapezoidal intuitionistic Type 2 fuzzy number(TrIT2FN) with arithmetic operations and solve an assignment problem using Hungarian method for Trapezoidal intuitionistic Type 2 fuzzy number (TrIT2FN).