Following the theoretical studies of J.B. Robinson and H.W. Kuhn in the late 1940s and the early 1950s, G.B. Dantzig, R. Fulkerson, and S.M. Johnson demonstrated in 1954 that large instances of the TSP could be solved by linear programming. Their approach remains the only known tool for solving TSP instances with more than several hundred cities; over the years, it has evolved further through the work of M. Grötschel, S. Hong, M. Junger, P. Miliotis, D. Naddef, M. Padberg, W.R. Pulleyblank, G. Reinelt, G. Rinaldi, and others. We enumerate some of its refinements that led to the solution of a 13,509-city instance.

Load balancing is a critical factor in achieving optimal performance in parallel applications where tasks are created in a dynamic fashion. In many computations, such as state space search problems, tasks have priorities, and solutions to the computation may be achieved more efficiently if these priorities are adhered to in the parallel execution of the tasks. For such tasks, a load balancing scheme that only seeks to balance load, without balancing high priority tasks over the entire system, might result in the concentration of high priority tasks (even in a balanced-load environment) on a few processors, thereby leading to low priority work being done. In such situations a load balancing scheme is desired which would balance both load and high priority tasks over the system. In this paper, we describe the development of a more efficient prioritized load balancing strategy. 1 Introduction Load balancing is a critical factor in achieving optimal performance in parallel applications w...

Most parallel programming models provide a single generic mode in which processes can exchange information with each other. However, empirical observation of parallel programs suggest that processes share data in a few distinct and specific modes. We argue that such modes should be identified and explicitly supported in parallel languages and their associated models. The paper describes a set of information sharing abstractions that have been identified and implemented in the parallel programming language Charm. It can be seen that using these abstractions leads to improved clarity and expressiveness of user programs. In addition, the specificity provided by these abstractions can be exploited at compile-time and at run-time to provide the user with highly refined performance feedback and intelligent debugging tools. 1 Introduction A typical parallel computation can be characterized as a collection of processes running on multiple processors. Depending on the programming model and lan...

Abstract. This paper presents a variation of the Euclidean Traveling Salesman Problem (TSP), the Multiple Traveling Salesman Problem (MTSP), and compares a variety of evolutionary computation algorithms and paradigms for solving it. Techniques implemented, analyzed, and discussed herein with regard to MTSP include use of a neighborhood attractor schema (a variation on k-means clustering), the "shrink-wrap " algorithm for local neighborhood optimization, particle swarm optimization, Monte-Carlo optimization, and a range of genetic algorithms and evolutionary strategies. 1

This paper will appear in `Twenty-five years of operations research in the Netherlands: papers dedicated

ion Hierarchies for Planning Actions with Context-dependent Effects Fr'ed'erick Garcia Robotics Laboratory Computer Science Department Stanford University garcia@flamingo.stanford.edu (415) 723-1604 Abstract In this paper we describe the automatic generation of abstraction hierarchies for planning within the state-variable formalism allowing actions with context-dependent effects previously presented. First we recall the principal notions of this formalism, as the essential facts, deduced facts, mono-variable actions, and also the SNLP-like planning algorithm we have defined. Then we consider the formalization of abstraction hierarchies within the STRIPS formalism, and we extend it by using mono-variable actions and contextdependent effects. In our model, the definition of a 'good' criticality function is no more only dependent on the action description, but also on the logical relations present between the literals. Under some conditions which have a meaningful interpretation in the...

This paper describes one improvement to "nearest neighbor" method for solving "traveling salesman" problem. This improvement finds sub optimal solutions (heuristic), so as original method. The only algorithms that are known are of exponential execution time, because "traveling salesman" problem is NP complete ([4]). These algorithms are correct theoretically, and they easily find solution for small number of graph nodes (N<20), but for large number of nodes (N>20) execution time is enormously large. Therefore, this paper describes improvement of "nearest neighbor" method, which gives suboptimal solutions (heuristic), but has O(n 2 ) execution time. AMS Subject Classifications: 90C27 Combinatorial programming 90C35 Network programming 90-08 Computational methods (optimization) 1 University of Belgrade, Faculty of Mechanical Engineering, Department of Mathematics, 27. marta 80. 1. Introduction 1.1 NP complete problems There is a class of problems which have not been theoreti...

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From bricks to brains: the embodied cognitive science of LEGO robots