In This paper a two stages Hybrid Flow Shop (HFS) problem with sequence dependent set up times is considered in which the preemption is also allowed. The objective is to minimize the weighted sum of completion time and maximum tardiness. Since this problem is categorized as an NP-hard one, meta-heuristic algorithms can be utilized to obtain high quality solutions in a reasonable amount of time. In this paper a Genetic algorithm (GA) approach is used and for parameter tuning the Response Surface Method (RSM) is applied to increase the performance of the algorithm. Computational results show the high performance of the proposed algorithm to solve the generated problems.

The dangers of poor pilot performance as well as time and place conditions, and such as low altitude and climate, damage critical aircraft control system. Mentioned factors, caused damage in control of sensitive military and research aircraft. So researchers had to find a solution for this problem. Therefore, the use of Unmanned Aircraft Vehicle or (UAV) in sensitive and important Operation is required. Furthermore, designing the controller system is one of the main discussed Dynamic issues in Flying Objects. Here, has been attempted to determine the optimal coefficients of PID1 controller in Autopilot based on Optimization Algorithm such as Evolutionary Algorithms in order to regulate and desired control on height of an Unmanned Aircraft Vehicle (UAV). The proposed Cost Function simultaneously optimized the system performance specifications. Optimization done by using Evolutionary Algorithms such as \(GA^2\), \(PSO^3\) and Social Policy Optimization Algorithm or \(ICA^4\). Finally, the system response based on Evolutionary Algorithms compared with the advantages of Classic method Optimization based on Constraints design, then you can easily decide on the superiority and being optimized of the proposed system.

In time diagnosis of diabetes significantly reduces damages and inconveniences of this disease in society. It may be said that one of the most important problems of diagnosis methods of this disease, particularly in early phases, is not to pay attention to proper features in order to diagnose the disease and as a result weakness in disease diagnosis. This research endeavors to introduce a new method for accurate diagnosis of this disease through usage of a combination of artificial intelligent methods such as fuzzy systems for immediate and accurate decision making, Evolutionary Algorithms (ACO1) for choosing best rules in fuzzy systems, and artificial neural networks for modeling, structure identification, and parameter identification. The proposed system relying on features of database in the form of combination and interaction succeeded in reaching an accuracy of 95.852% which in comparison to current methods on the one hand and to artificial methods in foresaid references on the other hand, has a proper and very faster performance than other intelligent methods and you can see its accuracy and excellence as an intelligent system.

Rising energy costs and environmental problems, also the limited resources of fossil fuels, have forced humans to use renewable energy. One of the systems that are commonly used is the cooling system, by means of solar energy. Since these models have a wide variety of effective variables, such as the temperature, latitude, the length of the day, also due to the nonlinear relationship between the variables, conventional modeling methods are inefficient. Therefore regarding to the extensive capabilities of simulation models in modeling a wide range of variables with complex and nonlinear relationship, in this study with the approach of simulating solar systems, an effective survey in the field of economic and efficiency of such systems, is presented.

In this paper, we present a method for solving time varying fractional optimal control problems by Bernstein polynomials. Firstly, we derive the Bernstein polynomials (BPs) operational matrix for the fractional derivative in the Caputo sense, which has not been undertaken before. This method reduces the problems to a system of algebraic equations. The results obtained are in good agreement with the existing ones in open literatures and the solutions approach to classical solutions as the order of the fractional derivatives approach to 1.

A change of Finsler metric \(F(x,y)\rightarrow \bar{F}(x,y)\) is called a Randers \(\beta\)-change of \(F\), if \(\bar{F}(x,y) = F(x,y) + \beta(x,y)\), where \(\beta(x,y)=b_i(x)y^i\) is a one-form on a smooth manifold \(M\). The purpose of the present paper is devoted to studying the conditions for more generalized m-th root metrics \(\tilde{F}_1= \sqrt{A_1^{\frac{2}{m_1}}+B_1+C_1}\) and \(\tilde{F}_1= \sqrt{A_2^{\frac{2}{m_2}}+B_2+C_2}\), when is established Randers \(\beta\)-change.

Let \(A\) and \(B\) be unital semi-simple Banach algebras. If \(B\) is a liminal \(C^*\)-algebra and \(\varphi\) is a surjective spectrum preserving linear mapping from \(A\) to \(B\), then \(\varphi\) is a Jordan homomorphism.

In this paper we propose an improved algorithm for scheduling grid workflow by using ant colony optimization method. Ant colony optimization (ACO) is a meta-heuristic for combinatorial optimization problems. JSWA algorithm is measured by using parameters such as reliability, cost, request and acknowledgement time and bandwidth. Regarding the proposed algorithm and its comparison with scheduling algorithm, we have established a new competency through which the tasks are carried out by considering preference criterion parameters. To do so, there should be less time complexities in accessing tasks for the present algorithms compared with the proposed one. By implementing a technical method we could consider a system in which the efficiency and optimization are increased and finally the time needed for program performance is decreased by using the target function. Also we could estimate the real time of tasks' commute by calculating the commute time compared with the previous algorithms. The result is that JSWA is more efficient than the algorithms such as ACS and MOACO.