Probabilistic constraints represent a major model of stochastic optimization. A possible approach for solving probabilistically constrained optimization problems consists in applying nonlinear programming methods. In order to do so, one has to provide sufficiently precise approximations for values and gradients of probability functions. For linear probabilistic constraints under Gaussian distri...

Structural optimization, when approached by conventional (gradient based) minimization algorithms presents several difficulties, mainly related to computational aspects for the huge number of nonlinear analyses required, that regard both Objective Functions (OFs) and Constraints. Moreover, from the early '80s to today's, Evolutionary Algorithms have been successfully developed and applied as a ...

We derive compact representations of BFGS and symmetric rank one matrices for optimization These representations allow us to e ciently implement limitedmemory methods for large constrained optimization problems In particular we discuss how to compute projections of limited memory matrices onto subspaces We also present a compact representation of the matrices generated by Broyden s update for s...

This paper describes the application of model predictive controllers for decentralized control and coordination of autonomous vehicle platoons. Information about the road trajectory and surrounding vehicles are used to solve a constrained nonlinear optimization problem to plan the system behavior over a finite horizon. System actuation restrictions are taken into consideration in the controller...

Bilevel problem formulations have received considerable attention as an approach to multidisciplinary optimization in engineering. We examine the analytical and computational properties of one such approach, collaborative optimization. The resulting system-level optimization problems su er from inherent computational di culties due to the bilevel nature of the method. Most notably, it is imposs...

In this chapter, we present a survey of constraint-handling techniques based on evolutionary multiobjective optimization concepts. We present some basic definitions required to make this chapter self-contained, and then we introduce the way in which a global (single-objective) nonlinear optimization problem is transformed into an unconstrained multiobjective optimization problem. A taxonomy of ...

In this series of papers, we present a motion planning framework for planning comfortable and customizable motion of nonholonomic mobile robots such as intelligent wheelchairs and autonomous cars. In this first one we present the mathematical foundation of our framework. The motion of a mobile robot that transports a human should be comfortable and customizable. We identify several properties t...

Nonlinear constrained optimization problems are encountered in many scientific fields. To utilize the huge calculation power of current computers, many mathematic models are also rebuilt as optimization problems. Most of them have constrained conditions which need to be handled. Borrowing biological concepts, a study is accomplished for dealing with the constraints in the synthesis of a four-ba...