A study on Bee algorithm and A* algorithm for pathfinding in games

Sabri, Aimi Najwa; Radzi, Nor Haizan Mohamed; Samah, Azurah A.

doi:10.1109/iscaie.2018.8405474

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…g(n) = the actual cost of a starting point to a specific point h(n) = heuristic distance f(n) = the accumulative between the actual point and the heuristic distance Figure 1. A* Algorithm Demonstration Sabri et al [5] proposed experiments with two scenarios: with obstacles and without obstacles. They also used various sizes of the map.…”

Section: A* Algorithmmentioning

confidence: 99%

A Review of Pathfinding in Game Development

Pardede

Athallah²,

Huda³

et al. 2022

CEPAT

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Pathfinding is one important method in many studies or works that consists of autonomous movement, such as robot, game, transportation, and so on. Pathfinding aims to find the most efficient route for the related autonomous entity. To date, there are many algorithms regarding to the pathfinding. Especially, there are four well-known pathfinding algorithms: A*, Theta*, Dijkstra, and Breadth First Search (BFS). Due to this circumstance, this work aims to observe and review these four well-known algorithms deeper. The discussion consists of the conceptual model or framework, the formalization, and the implementation. The result shows that these algorithms have been implemented in many game so that they are promising to be used in the future game development.

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Section: A* Algorithmmentioning

confidence: 99%

A Review of Pathfinding in Game Development

Pardede

Athallah²,

Huda³

et al. 2022

CEPAT

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“…This paper's findings are in line with the findings of other research groups using SI-based pathfinding algorithms. For example, Bee Algorithm pathfinding-a SI-based pathfinding algorithm-was found to be, both, more computationally and memory efficient then the A* algorithm and that the efficiency disparity grew larger as the environment size increased [25].…”

Section: Computational and Memory Performancementioning

confidence: 99%

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

Pyke¹,

Stark

2021

Front. Appl. Math. Stat.

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In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.

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“…Similarly, a study on the Bee algorithm has been conducted by Sabri et al [41] and has been compared with A* on two different maps and two distinct measurements, performance, and memory consumption. For performance, on average, the A* algorithm surpasses the Bee algorithm on the free obstacle's map (200x200 and 500x500).…”

Section: Optimization By Combining Search Algorithmsmentioning

confidence: 99%

“…There are several studies using swarm intelligence in the pathfinding technique for the game's navigation in comparison with the others [41] [42] [43]. The potential of the hybrid algorithm shall not be undervalued since the algorithmic structure of swarm intelligence achieves well in increasing CPU performance.…”

Section: Summary and Future Trendsmentioning

confidence: 99%

A Review on Informed Search Algorithms for Video Games Pathfinding

Kapi¹

2020

IJATCSE

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Pathfinding is a broadly applied algorithm that involved the discovery of routes between two positions by avoiding obstacles at the same time. Recently, a significant number of researchers focusing on informed search algorithms for pathfinding concerning games. However, review regarding the latest optimization in the pathfinding algorithm and its advantages still lacks in the literature. To organize this heterogeneity, this paper presents a review that focused on numerous modifications to enhance the execution of the informed search algorithm through four classified perspectives: i) modification to the graph representation, ii) enhancement of heuristic function, iii) hybrid search algorithm, and iv) new data structure. This paper also aims to discuss common challenges faced by pathfinding in video games and providing future trends for optimization. While incorporating pathfinding optimization over the past decade, this paper also aims to assist new researchers by emphasizing the potential path for further exploration.

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A study on Bee algorithm and A* algorithm for pathfinding in games

Cited by 10 publications

References 3 publications

A Review of Pathfinding in Game Development

A Review of Pathfinding in Game Development

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

A Review on Informed Search Algorithms for Video Games Pathfinding

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