Index Term— Interative Deepining Bi-directional Heuristic Front-to-Front Algorithm (IDBHFFA), Bi-directional Heuristic Front-to-Front Algorithm (BHFFA), Bi-directional Depth-First Iterative Deepening (DFID) S earch, Bi-directional Heuristic Path (original) (raw)

A New Approach of Iterative Deepening Bi- Directional Heuristic Front-to-Front Algorithm (IDBHFFA)

2010

Artificial Intelligence (AI) is a subject that studies techniques for making computers exhibit intelligent behavior. S earching still remains one of the problem in AI. Bi -directional search is performed by searching simultaneously in forward direction from the initial node and in backward direction from the goal node. Bi-directional heuristic search algorithms need less time and space than their unidirectional versions. Bi -directional Heuristic Front to Front Algorithm (BHFFA) is one of the Bi - directional heuristic search algorithm. However, it has some disadvantages. It needs to store many unnecessary nodes prior to termination. Moreover, in large problem spaces the computational overhead for the selection of the next node to be expanded increases significantly. This paper presents a modification to the BHFFA called Iterative Deepening Bi - directional Heuristic Front-to-Front Algorithm (IDBHFFA) that has been analyzed and implemented using the 8-puzzle problem. The proposed al...

GBFHS: A Generalized Breadth-First Heuristic Search Algorithm

2018

Recently there has been renewed interest in bidirectional heuristic search. New algorithms, e.g., MM, MMe, and NBS, have been introduced which seem much closer to refuting the accepted wisdom that “any front-to-end bidirectional heuristic search algorithm will likely be dominated by unidirectional heuristic search or bidirectional brute-force search” . However, MM and MMe can still be dominated by their bidirectional brute-force versions, i.e., they can show a “humpin-the-middle”. We introduce a novel general breadth-first heuristic search algorithm, GBFHS, that unifies both unidirectional and bidirectional search into a single algorithm. It uses knowledge of the edge cost in unit cost domains to stop on first-collision in unidirectional search and in bidirectional search, unlike MM, MMe, and NBS. With no heuristic it expands fewer nodes bidirectionally than Nicholson’s blind bidirectional search algorithm. GBFHS expands substantially fewer nodes than MM0, MM, MMe, and NBS. Addition...

Bidirectional Heuristic Search Reconsidered

Journal of Artificial Intelligence Research

The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited ...

PNBA*: A Parallel Bidirectional Heuristic Search Algorithm

A* (A-star) is a heuristic search algorithm used in various domains, such as robotics, digital games, DNA alignment, among others. In spite of its large use, A* can be a computationally expensive depending on the characteristics of the state space and heuristics used. Aiming at improving its performance, in this paper we propose a parallel implementation of a bidirectional version of A*. Named PNBA* (Parallel New Bidirectional A*) the proposed algorithm combines the benefits of bidirectional search and parallel execution in the development of an efficient A* based search algorithm. Experiments performed in different domains show that PNBA* is more efficient than the original A* and NBA*, the bidirectional version of A* it is based on.

Heuristic Searching: A* Search

IOSR Journal of Computer Engineering, 2012

Searching has a great impact on computer science. There are lots of searching algorithms. Among them, A* search algorithm is one of the most promising algorithm. A* search algorithm is more efficient than the other searching algorithm, because of its heuristic characteristic. A* search is widely used in path finding and graph traversal among the points, called nodes. Due to the heuristic characteristic A* search must find the goal with the minimum cost, if obviously there is a goal state. For this reason there are many application of this. The aim of this paper is to understand the A* search technique, its characteristics, mathematical representation, some graphical representation.

Heuristic and Meta-Heuristic Algorithms and Their Relevance to the Real World: A Survey

— Nowadays computers are also used to solve incredibly complex problems. To solve these problems we have to develop some advanced algorithms. Exact algorithms of such problems might need unacceptably huge time & space to discover the solutions. For making the solution-finding algorithms acceptable approximation algorithms have been developed. These approximation algorithms use the heuristics and meta-heuristics functions to find out the solutions. Heuristic algorithms use the special designed functions to find out solution space intelligently. Meta-heuristics algorithms are the iterative generation process which guides a subordinate heuristic for exploring and exploiting the search space. Learning strategies in meta-heuristics helps to find efficient near-optimal solutions. Meta-heuristic algorithms make the complex problems solvable in acceptable time. This survey paper is trying to explain heuristic and Meta-heuristic techniques to solve the complex problems.