The traditional question of optimally deciding when to cut down a tree is among the most commonly posed questions asked of students learning the technique of dynamic programming. This paper shows that the traditional tree-cutting example is improperly formulated when the question of replanting is addressed, derives the proper method of finding optimal harvest length, and applies this method to ...

Efficient solution of large-scale dynamic optimization problems can be achieved by exploiting the advantages of state-of-the-art integration and large-scale nonlinear programming tools. TruncatedNewton method provides an effective way to solve large-scale optimization problems by achieving savings in computation and storage. For dynamic optimization, the Hessian-vector products required by thes...

This paper presents biped walking control using a library of optimal trajectories. The biped walking control problem is formulated as an optimal control problem. We take advantage of a parametric trajectory optimization method to find the periodic steady-state trajectory in regular walking. Then we use Differential Dynamic Programming (DDP) to generate a library of optimal trajectories and loca...

We present a data-driven, probabilistic trajectory optimization framework for systems with unknown dynamics, called Probabilistic Differential Dynamic Programming (PDDP). PDDP takes into account uncertainty explicitly for dynamics models using Gaussian processes (GPs). Based on the second-order local approximation of the value function, PDDP performs Dynamic Programming around a nominal traject...

The Taguchi method is a useful technique to improve the performance of products or processes at a lower cost and in less time. This procedure can be categorized in the static and dynamic quality characteristics. The optimization of multiple responses has received increasing attention over the last few years in many manufacturing organizations.  Several approaches dealing with multiple static q...

optimization techniques can be efficiently utilized to achieve an optimal shape for arch dams. this optimal design can consider the conditions of the economy and safety simultaneously. the main aim is to present an applicable and practical model and suggest an algorithm for optimization of concrete arch dams to enhance their seismic performance. to achieve this purpose, a preliminary optimizati...

In optimal control problems of switched systems, we may need to find both an optimal continuous input and an optimal switching sequence since the system dynamics vary before and after every switching instant. In this paper, optimal control problems for both continuoustime and discrete-time switched systems are formulated and investigated. In particular, we regard an optimal control problem as a...

Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimiz...

BACKGROUND AND PURPOSE A behavioral intervention is a program aimed at modifying behavior for the purpose of treating or preventing disease, promoting health, and/or enhancing well-being. Many behavioral interventions are dynamic treatment regimens, that is, sequential, individualized multicomponent interventions in which the intensity and/or type of treatment is varied in response to the needs...

this paper presents a parameterization method to optimal trajectory planning and dynamic obstacle avoidance for omni-directional robots. the aim of trajectory planning is minimizing a quadratic cost function while a maximum limitation on velocity and acceleration of robot is considered. first, we parameterize the trajectory using polynomial functions with unknown coefficients which transforms t...