Depth-first search

Abstract: Richard E. Korf** Department of Computer Science, Columbia University. New York, NY 10027, U.S.A. 97 The complailits of various search algorithms are consuured in ~rms of time. space. and cost of solwion path. It is known that breadth·first search requires tOO much space and depth·first Starch can UM too much time and d~n't always find a cheapest path. A depth·first iterative·deepening algorithm is shown to be asymptotically optimal along all three dimmsions for exponential tree searches. The algorithm has been used succt$Sfully in chess programs, has bun effectively combined with bi-directional search. and has been applied to best·first heuristic search as well. This heuristic depth·first iterative· deePening algorithm is the only known algorithm that is capable of finding optimal solUtions to randomly generated instances of the Fif/un Puzzle within practical resource

Abstract: Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better candidate solutions in each iteration and hence traverse a more promising search path. Starting from the large neighborhood search method, we give an overview of very large scale neighborhood search methods and discuss recent variants and extensions like variable depth search and adaptive large neighborhood search.

Abstract: This paper presents several modifications to the basic rapidly-exploring random tree (RRT) search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the search. Results from a relevant simulation experiment illustrate the benefit and drawbacks of the developed algorithms. The paper concludes with several promising directions for future research.