Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these ...

1. Abstract In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. Aleatory uncertainty is classified as objective and irreducible uncertainty with sufficient information on input uncertainty data, whereas epistemic uncertainty is a subjective and reducible uncertainty that stems from lack of knowledge on input uncertainty ...

this paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. there are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. every option should be assessment based on the different criteria such as cost, mass, reliability and technology contraint (complexity). in this rese...

In this paper particle swarm optimization(PSO)has been applied for the design of the optimal robust controllers for third order systems . The controller design problem is posed as constrained nonlinear optimization problem. The parameters of the chosen controller are obtained solving the nonlinear constrained optimization problem.The performance index which has been used in the design is integr...

Mathematical optimization plays an important role in engineering design, leading to greatly improved performance. Deterministic optimization however, can lead to undesired choices because it neglects input and model uncertainty. Reliability-based design optimization (RBDO) and robust design improve optimization by considering uncertainty. A design is called reliable if it meets all performance ...

background and objectives: a sudden jump in blood demand during natural disasters may have strong negative impact on the performance of blood supply chain. appropriate response to the emergency situations, requires predictive approach to determining the optimal allocation of blood supply chain resources for various disaster scenarios. the present study, thus, presents an optimization model aime...

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems so far. In the present study, a simple optimization (SOPT) algorithm with two main steps namely exploration and exploitation, is provided for practical applications. Aside from a reasonable rate of convergence attained, the ease in its implementation and dependen...

The ever-shorter time-to-market calls for efficient robust IC design algorithms. Robust circuits satisfy all design requirements across a range of operating conditions and manufacturing process variations. In the paper we propose an automated robust IC design and optimization process derived from the design algorithms utilized manually by experienced analog IC designers. We achieve this by tran...

in this paper, a robust model presented is developed for hub covering flow problem in which hubs have capacity. this problem has two objective functions converting to a single-objective problem by using weighting method. firstly, the problem will be formulated in a certain case. then, by considering the demand as a non-random variable, it will be modeled by robust optimization. finally, by usin...