2019
DOI: 10.1504/ijcsm.2019.097636
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T*: a weighted double-heuristic search algorithm to find the shortest path

Abstract: This paper proposes a weighted double-heuristic search algorithm to find the shortest path between two points. It can be used in numerous fields such as graph theory, game theory, and network. This algorithm, called T*, uses a weighted and heuristic function as f(x) = × t(x) + × h1(x) + γ × h2(x). It selects the path which minimises f(x) where x is a current node on the path, t(x) is cost of the path from start to x, h1(x) is a heuristic to estimate the cost from x to the straight line passing through start an… Show more

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Cited by 6 publications
(6 citation statements)
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“…To solve diverse applications, numerous algorithms are proposed. [29][30][31] The bat algorithm 32,33 is among the classic swarm intelligence optimization techniques [34][35][36] that used the principle of echolocation of the bat to discover the optimum solution within the scope of the problem. Several researchers have proved this algorithm to verify its feasibility.…”
Section: Related Workmentioning
confidence: 99%
“…To solve diverse applications, numerous algorithms are proposed. [29][30][31] The bat algorithm 32,33 is among the classic swarm intelligence optimization techniques [34][35][36] that used the principle of echolocation of the bat to discover the optimum solution within the scope of the problem. Several researchers have proved this algorithm to verify its feasibility.…”
Section: Related Workmentioning
confidence: 99%
“…In order to optimise the performance of A* algorithm, WA* [23] became the variant of A* by weighting the heuristic function h ( n ). Gharajeh [24] improved A* by introducing the weighted double‐heuristic function to find the shortest path and proposed the T* algorithm. It optimised the function of A* by adding three different parameters: to weight the cost of path from the source node to the current node, the estimate cost of the minimum cost path from the current node to goal node and the straight path through the start node and the goal node from the current node, respectively.…”
Section: Introductionmentioning
confidence: 99%
“…21 The principle of feature selection is to improve the classification performance by simplifying the original dataset and select the optimal feature subset. Since the concept of swarm intelligence [22][23][24] was proposed by Hackwood and Beni in 1992, it has been widely used in various fields. At its core, several simple individuals form a group, which demonstrates advanced and complex functions through mechanisms such as cooperation, competition, interaction, and learning.…”
Section: Introductionmentioning
confidence: 99%