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State of the Art in Parallel Search Techniques for Discrete Optimization Problems
January/February 1999 (vol. 11 no. 1)
pp. 28-35

Abstract—Discrete optimization problems arise in a variety of domains, such as VLSI design, transportation, scheduling and management, and design optimization. Very often, these problems are solved using state space search techniques. Due to the high computational requirements and inherent parallel nature of search techniques, there has been a great deal of interest in the development of parallel search methods since the dawn of parallel computing. Significant advances have been made in the use of powerful heuristics and parallel processing to solve large-scale discrete optimization problems. Problem instances that were considered computationally intractable only a few years ago are routinely solved currently on server-class symmetric multi-processors and small workstation clusters. Parallel game-playing programs are challenging the best human minds at games like chess. In this paper, we describe the state of the art in parallel algorithms used for solving discrete optimization problems. We address heuristic and nonheuristic techniques for searching graphs as well as trees, and speedup anomalies in parallel search that are caused by the inherent speculative nature of search techniques.

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Index Terms:
Parallel processing, discrete optimization, tree search, load balancing, backtracking, heuristic search, game-tree search, speedup anomalies.
Citation:
Ananth Grama, Vipin Kumar, "State of the Art in Parallel Search Techniques for Discrete Optimization Problems," IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 1, pp. 28-35, Jan.-Feb. 1999, doi:10.1109/69.755612
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