Challenges such as advances in technology, demands of the global market, and limited warehouse spaces resort manufacturing industries to subcontracting. Subcontracting has been a considerable alternative in the manufacturing industries and is utilized as a strategic tool to diminish operation costs primarily to address the problem of scarcity when the firm faces a large demand on the commodity it supplies. The present study employed a mathematical model among firms engaging in subcontracting in search of an optimal schedule in the manufacture of the product and distribution of production time involved with an objective of obtaining a maximum profit. The constraints in the mathematical formulation included the total demand, processing capacity, available supply, processing rate, and time. The plausibility and the possible utility of the mathematical model has been explored employing Sequential Quadratic Programming algorithm in the search of the optimal solutions.

In this article, with the idea of the Sequential Quadratic Programming method and using the smoothed l0 norm as the objective function, a modified Sequential Quadratic Programming method is presented to solve the problem of finding sparse solutions of the system of underdetermined linear equations. We provide a new approach for solving Quadratic subproblems, which leads to the less complexity and simplicity in solving Quadratic subproblems. The proposed method starts with an initial guess and in each iteration to calculate the search direction, a specific Quadratic optimization problem is solved. The Quadratic approximation of the objective function and the linear approximation of the constraints of the original problem are used to design the subproblem. Then, theoretical analysis of the method is presented and its convergence is proved. The results obtained from the implementation of the proposed method on sensor matrices of different dimensions show that the efficiency of the method does not depend on the dimensions of the input matrix. Finally, the comparison of the reported SNR regarding to the proposed method with the most frequent thin signal recovery algorithms shows the high efficiency and performance of the proposed method.

Sparse representation has many applications in signal and image processing, including applications in medical image reconstruction, image enhancement and compression, signal separation, array and radar signal processing. This importance has caused the researchers to benefit from a variety of sparse representation method and to use different norms to solve optimization problems. In this article, firstly, different methods of solving the sparse representation are reviewed, and then a new and efficient method is presented to recover noisy sparse signals with the benefit of Sequential Quadratic Programming and smoothed L0 norm. The results of the experiments show the high success rate of the proposed method compared to other sparse representation optimization methods.

This paper aims to show the capability of hybrid optimization algorithms in finding the proper optimal plan for optimizing complex systems. So design optimization of an unmanned aerial vehicle has been presented as a complicated system by using multidisciplinary design optimization, genetic algorithm, and hybrid optimization algorithm. This study uses a hybrid optimization algorithm from a genetic algorithm as a global optimizer and from Sequential Quadratic Programming as a local optimizer. The optimization problem of this study is a multi-objective design optimization problem in which the considered objective functions are the minimization of takeoff weight and cruise drag force. The considered constraints are related to the deflection of the control surface, stability, and handling quality specifications (damping coefficients, natural frequencies, and time constants). The proposed design optimization problem has been solved by using a hybrid optimization algorithm and genetic algorithm separately, and their results have been compared to each other. Although both optimal designs are acceptable, results show that the optimal design of the hybrid optimization algorithm is better than the optimal design of the genetic algorithm from an objective functions point of view. This issue shows the good performance of a hybrid optimization algorithm for design optimization of complex systems.

One of the most common causes of death during the birth of babies is heart failure. Diagnosis of this disease requires observation of heart activity. Since the electrical signals recorded in the mother’s abdomen contain a lot of information such as: mother’s heart signal, mother’s and fetus’s muscle activity, fetus’s brain activity and environmental noises, researchers are looking for ways to separate the fetus’s heart signals from the mother’s are. The proposed method has super-linear convergence, which provides global convergence results and an exact solution to solve the sub-problem. The performance of the proposed method is compared with the best existing methods and the results show that the proposed method has the lowest error rate and the highest speed in separating fetal heart signals from the mother compared to other methods.

This paper presents a fuzzy approach to the prediction of highly nonlinear time series.The optimized Mamdani-type fuzzy system denoted SQP-FLC is applied for the input-output modeling of measured data. In order to tune fuzzy membership functions, a Sequential Quadratic Programming (SQP) method is employed. The proposed method is evaluated and validated on a highly complex time series, daily gold price data. The time series is primarily investigated for its chaotic properties.Correlation dimension and autocorrelation function (ACF) for the time series are discussed. Accordingly, time delay and embedding dimension are computed. Month selection in each stage is based on computed correlation coefficients. Thus, for the proposed fuzzy predictor, 3, 5, and 7 dynamics are selected and the time series are verified. The simulation results for one-step-ahead prediction of daily gold price in 2010, compared with methods of ANFIS and GA-FLC, demonstrate comparably better performance of the proposed SQP-FLC until the higher significant dynamics of the chaotic trend is taken into account.

The real-world applications addressing the nonlinear functions of multiple variables could be implicitly assessed through structural reliability analysis. This study establishes an efficient algorithm for resolving highly nonlinear structural reliability problems. To this end, first a numerical nonlinear optimization algorithm with a new simple filter is defined to locate and estimate the most probable point in the standard normal space and the subsequent reliability index with a fast convergence rate. The problem is solved by using a modified trust-region Sequential Quadratic Programming approach that evaluates step direction and tunes step size through a linearized procedure. Then, the probability expectation method is implemented to eliminate the linearization error. The new applications of the proposed method could overcome high nonlinearity of the limit state function and improve the accuracy of the final result, in good agreement with the Monte Carlo sampling results. The proposed algorithm robustness is comparatively shown in various numerical benchmark examples via well-established classes of the first-order reliability methods. The results demonstrate the successive performance of the proposed method in capturing an accurate reliability index with higher convergence rate and competitive effectiveness compared with the other first-order methods.

This paper presents a fuzzy goal Programming (FGP) methodology for solving bi-level Quadratic Programming (BLQP) problems. In the FGP model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vector of fuzzy goals of the decision variables controlled by decision maker at the first level are developed in the model formulation of the problem. To achieve the highest membership value of each of the fuzzy goals, we formulate the problem by minimizing the negative deviational variables and thereby obtaining the most satisfactory solution for all decision makers. A numerical example is given to demonstrate the proposed approach.