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A* search algorithm

About: A* search algorithm is a research topic. Over the lifetime, 1656 publications have been published within this topic receiving 23080 citations. The topic is also known as: A star search algorithm & A-star algorithm.


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Journal ArticleDOI
TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

5,501 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed black hole algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.

963 citations

Journal ArticleDOI
TL;DR: A binary version of the gravitational search algorithm, based on the law of gravity and mass interactions, is introduced and the experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
Abstract: Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.

702 citations

Journal ArticleDOI
TL;DR: In this article, the main body of the article introduces several modifications (Basic Theta*, Phi*) and improvements (RSR, JPS) of A star algorithm, focused primarily on computational time and the path optimality.

488 citations

Journal ArticleDOI
TL;DR: A graph matching approach for solving the task assignment problem encountered in distributed computing systems with a cost function defined in terms of a single unit, time, and a new optimization criterion, called the minimax criterion, based on which both minimization of interprocessor communication and balance of processor loading can be achieved.
Abstract: A graph matching approach is proposed in this paper for solving the task assignment problem encountered in distributed computing systems. A cost function defined in terms of a single unit, time, is proposed for evaluating the effectiveness of task assignment. This cost function represents the maximum time for a task to complete module execution and communication in all the processors. A new optimization criterion, called the minimax criterion, is also proposed, based on which both minimization of interprocessor communication and balance of processor loading can be achieved. The proposed approach allows various system constraints to be included for consideration. With the proposed cost function and the minimax criterion, optimal task assignment is defined. Graphs are then used to represent the module relationship of a given task and the processor structure of a distributed computing system. Module assignment to system processors is transformed into a type of graph matching, called weak homomorphism. The search of optimal weak homomorphism corresponding to optimal task assignment is next formulated as a state-space search problem. It is then solved by the well-known A* algorithm in artificial intelligence after proper heuristic information for speeding up the search is suggested. An illustrative example and some experimental results are also included to show the effectiveness of the heuristic search.

358 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202317
202259
202190
202096
2019100
2018108