The most important issue in multi-objective optimization problems is to determine the Pareto points along the Pareto frontier. If the optimization problem involves multiple conflicting objectives, the results obtained from the Pareto-optimality will have the trade-off solutions that shaping the Pareto frontier. Each of these solutions lies at the boundary of the Pareto frontier, such that the i...

controller design and optimization problems, with more than one objective, are referred as multiple objectives or multiple attributed problems. in this paper, a novel method is proposed for designing optimum pid controller that is called genetic multiple attributed decision making method (gmadm). this method is newer than the previous methods and in this paper some options are considered that h...

A hierarchical approach to robust design optimization considering the needs of robust performance from multiple disciplines is proposed in this paper. The approach extends the framework of Collaborative Optimization to perform robust design with multiple objectives from different disciplines under uncertainty. The proposed approach is demonstrated by examples and compared with the all-in-one ap...

The family of optimization problems with value function objectives includes the minmax programming problem and the bilevel optimization problem. In this paper, we derive necessary optimality conditions for this class of problems. The main focus is on the case where the functions involved are nonsmooth and the constraints are the very general operator constraints.

there have been many productive methods developed so far for optimization of multiple response surface (mrs) problems. this paper tends to review the most seminal approaches in mrs and discuss the strength and weakness of each of the approaches through existing aspects in mrs. a numerical example is included to compare results by different methods. finally some of the prominent areas for future...

In intractable, undirected graphical models, an intuitive way of creating structured mean field approximations is to select an acyclic tractable subgraph. We show that the hardness of computing the objective function and gradient of the mean field objective qualitatively depends on a simple graph property. If the tractable subgraph has this property— we call such subgraphs v-acyclic—a very fast...