This paper proposes an improved salp swarm algorithm (ISSA) as an effective metaheuristic method for tackling global optimization issues and damping power system oscillations. In the suggested ISSA, new equations are introduced to update the location of the leader and followers. This modification improves the method's exploration possibilities while also preventing it from converging prematurely. Benchmark test functions are used to confirm the proposed algorithm's performance, and the results are compared to SSA and other effective optimization algorithms. According to the extensive comparisons, the enhanced ISSA algorithm has higher convergence accuracy and stability than the original SSA and other researched algorithms. Furthermore, the feasibility and efficiency of the proposed method were demonstrated by the simultaneous coordinated design of UPFC based damping controllers. For the two-area, four-machine system, the experimental findings are provided. Simulation experiments reveal that ISSA designed controllers outperform those created using other methods.

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Generally, the process of detecting micro expressions takes significant importance because all these expressions reflect the hidden emotions even when the person tried to conceal them. In this paper, a new approach has been proposed to estimate the percentage of sarcasm based on the detected degree of happiness of facial expression using fuzzy inference system. Five regions in a face (rightleft brows, rightleft eyes, and mouth) are considered to determine some active distances from the detected outline points of these regions. The membership functions in the proposed fuzzy inference system are used as a first step to determine the degree of happiness expression based mainly on the computed distances and then another membership function is used to estimate the percentage of sarcasm according the outcomes of the membership functions in the first step. The proposed approach is validated using some face images which are collected from the SMIC, SAMM, and CAS(ME)2 standard datasets.

The Landweber iteration method is one of the most popular methods for the solution of linear discrete ill-posed problems. The diversity of physical problems and the diversity of operators that result from them leads us to think about updating the main methods and algorithms to achieve the best results. We considered in this work the linear operator equation and the use of a new version of the Landweber iterative method as an iterative solver. The main goal of updating the Landweber iteration method is to make the iteration process fast and more accurate. We used a polar decomposition to achieve a symmetric positive definite operator instead of an identity operator in the classical Landweber method. We carried out the convergence and other necessary analyses to prove the usability of the new iteration method. The residual method was used as an analysis method to rate the convergence of the iteration. The modified iterative method was compared to the classical Landweber method. A numerical experiment illustrates the effectiveness of this method by applying it to solve the inverse boundary value problem of the heat equation (IBVP).

In this paper, the inverse problems for the boundary value and initial value in a heat equation are posed and solved. It is well known that those problems are ill posed. The problems are reformulated as integral equations of the first kind by using the separation-of-variables method. The discretization of the integral equation allowed us to reduce the integral equation to a system of linear algebraic equations or a linear operator equation of the first kind on Hilbert spaces. The Landweber-type iterative method was used in order to find an approximation solution. The V-cycle multigrid method is used to obtain more frequent and fast convergence for iteration. The numerical computation examples are presented to verify the accuracy and fast computing of the approximation solution.