n. danesh

To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.

The aim of this research is to introduce a semi-analytical approach for the analysis of the free and forced nonlinear vibrations of a bending shape memory alloy (SMA) beam; while, considering the effect of its pseudo-elastic behavior. In order to create a primary deflection, an appropriate pre-strain is applied to the SMA beam using a compression spring. A new material model was utilized to simulate the nonlinear hysteric behavior of the SMA beam, while the differential equations of motion of the beam were derived based on Euler–Bernoulli beam theory and Hamilton principle. The extracted nonlinear partial differential equations of motion are semi-analytically solved by utilizing the Galerkin method. The pseudo-elastic behavior and energy dissipation of the SMA beam were studied in the free and forced nonlinear vibration regimes. Finally, the influences of the system parameters such as the spring constant, amplitude, and frequency of the excitation force on the absorber efficiency were investigated, and its stability was studied. The numerical results depict that the SMA beam exhibits a highly nonlinear dynamical behavior, and can be used as an actuator for energy dissipation.

Today, different methods are being used to improve the properties of edible films; one of the most effective and commonly used ones is using nanometer-sized fillers and the production of polymer nanocomposites. The objective of the present study is to produce quince seed-based nanocomposite film reinforced with nanocrystalline cellulose and to study the properties of the resulting composite film.

In the first step, the quince seed mucilage was extracted and then, with different concentrations of nanocrystals cellulose (NCC) (3, 5, and 7%) and 35% (w/w) glycerol as plasticizer the nanocomposite film was produced by molding method. Then the physical, mechanical, barrier, thermal and structural properties of the films were examined.

Addition of nanocrystals increased the thickness of resulting films but decreased their moisture content, water solubility and water vapor permeability (WVP) to 7.2%, 29.3% and 5.6% when using 7% nanocrystal cellulose. Increasing of nanocrystals concentration in films resulted in an increase in a* and b* and a decrease in L*. Incorporation of nanocrystals also improved the mechanical properties of quince seed gum-based films including tensile strength and young module, whereas elongation at break was not significant. The glass transition temperature of films also was increased by the addition of nanocrystals which was determined by means of differential scanning calorimetry. FT-IR spectra of samples also approved the interaction between nanocrystals and quince seed gum.

The produced films exhibited good physical properties, reduced WVP, and enhanced mechanical properties, which are the main properties required for packaging applications.

To achieve significant throughput, interference alignment (IA) is an encouraging technique for wireless interference networks. In this study, we design an aligned beamformer based on the interference leakage minimization (ILM) method to reduce the interference power for a multiple-input multiple-output interference channel (MIMO-IC).
To deal with the non-convexity of ILM problem, we used a non-convex programming method (i.e., difference of convex [DC]). In this way, the interference leakage function is reformulated to a DC function including difference of two convex terms. Then, an additive function is defined that includes the DC objective function and a penalty function.
We propose a novel DC-based IA algorithm that uses solutions of an upper bound of the additive function in each iteration; as the initial state for the next iteration. Through an iterative manner and for the large values of the penalty factor, the solutions of upper bound function converge to the solutions of the original DC objective function (i.e., interference leakage function).
In contrast to the frequent IA methods, the proposed DC-based IA algorithm updates transmit- and receive-beamformers in each iteration jointly (not alternately). Simulation results indicate that the proposed method outperforms some competitive IA algorithms by providing more throughputs and less interference leakage.