Massively Parallel Heuristic Search for Approximate Optimization Problems

Abstract

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.