Massively Parallel Heuristic Search for Approximate Optimization Problems

Most admissible search algorithms fail to solve reallife problems because of their exponential time and storage requirements. Therefore, to quickljy obtain near-optimal solutions, the use of approximute algorithms and inadmissible heuristics are of practical interest. The use of parallel and distributed ahgorithms [l, 6, 8, 111 further reduces search complexity. I n this paper we present empirical results on a massively parallel search algorithm using a Connection .Machine CM-2. Our algorithm, PBDA', is based on the idea of staged search [9, lo]. Its execution time is directly proportional t o the depth of search, and solution quality is scalable with the number of processors. W e tested it on the 1Bpuzzle problem using both admissible and inadmissible heuristics. The best results gave an average relative error of 1.66% and 66% optimal solutions.