Controlled best-first search for near optimal solutions
Ambuj Mahanti, Subrata K. Ghosh · 2002
Many heuristic search algorithms are available for solving combinatorial optimization problems in artificial intelligence and operations research applications. However, most of these algorithms do not scale up in practice because of their time and/or storage limitations. The paper presents two algorithms, namely BDA* (breadth-depth-A*) and CA* (controlled A*), which can overcome both time and storage limitations at the expense of not guaranteeing optimal solutions at all times. The paper demonstrates the working of BDA* and CA* on the well known state space problems, namely 15-puzzle and 3-machine flow-shop scheduling problem. A new inadmissible heuristic is suggested for sliding tile puzzles. Detailed experimental results showing the effectiveness of the algorithms are also presented.>