Sharif Digital Repository

Reducing the burden of the remaining actuators through decreasing the performance gracefully is an important field in active fault tolerant control. According to the literature, two important points have been identified in the works considering graceful performance degradation: 1) using single-objective optimization, 2) assuming an engineering insight into the performance of the faulty system. This paper has two contributions: First, it is shown that in some cases, single-objective optimization may not be able to provide a satisfactory solution for the problem. Second, a new systematic and general method is proposed to remove the need for the engineering insight. The proposed method is based... 

In this study a multi-objective genetic algorithm is utilized to obtain a Pareto optimal set of solutions for geometrical characteristics of airfoil sections for 10-meter blades of a horizontal axis wind turbine. The performance of the airfoil sections during the process of energy conversion is evaluated deploying a 2D incompressible unsteady CFD solver and the second law analysis. Artificial neural networks are trained employing CFD obtained data sets to represent objective functions in an algorithm which implements exergetic performance and integrity characteristics as optimization objectives. The results show that utilizing the second law approach along with Pareto optimality concept... 

This study proposes a probabilistic dynamic model for multi-objective distributed generation (DG) planning, which also considers network reinforcement at presence of uncertainties associated with the load values, generated power of wind turbines and electricity market price. Monte Carlo simulation is used to deal with the mentioned uncertainties. The planning process is considered as a two-objective problem. The first objective is the minimisation of total cost including investment and operating cost of DG units, the cost paid to purchase energy from main grid and the network reinforcement costs. The second objective is defined as the minimisation of technical risk, including the probability... 

A two-machine flowshop scheduling problem is addressed to minimize setups and makespan where each job is characterized by a pair of attributes that entail setups on each machine. The setup times are sequence-dependent on both machines. It is shown that these objectives conflict, so the Pareto optimization approach is considered. The scheduling problems considering either of these objectives are N p - hard, so exact optimization techniques are impractical for large-sized problems. We propose two multi-objective metaheurisctics based on genetic algorithms (MOGA) and simulated annealing (MOSA) to find approximations of Pareto-optimal sets. The performances of these approaches are compared with...