11,336 research outputs found
Global Continuous Optimization with Error Bound and Fast Convergence
This paper considers global optimization with a black-box unknown objective
function that can be non-convex and non-differentiable. Such a difficult
optimization problem arises in many real-world applications, such as parameter
tuning in machine learning, engineering design problem, and planning with a
complex physics simulator. This paper proposes a new global optimization
algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both
fast convergence in practice and finite-time error bound in theory. The
advantage and usage of the new algorithm are illustrated via theoretical
analysis and an experiment conducted with 11 benchmark test functions. Further,
we modify the LOGO algorithm to specifically solve a planning problem via
policy search with continuous state/action space and long time horizon while
maintaining its finite-time error bound. We apply the proposed planning method
to accident management of a nuclear power plant. The result of the application
study demonstrates the practical utility of our method
Interaction between intelligent agent strategies for real-time transportation planning
In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach
- …