A new model to imitate the foraging behavior of Physarum polycephalum on a nutrient-poor substrate

Wu, Yuheng; Zhang, Zili; Deng, Yong; Zhou, Huan; Qian, Tao

doi:10.1016/j.neucom.2012.10.044

Cited by 10 publications

(10 citation statements)

References 22 publications

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“…( 2) where E ⊂ R 2 denote a closed environment. e synthetical field intensity is obtained by equations ( 1)-(3) [28]. If there is only one source in the environment E, the field intensity at the point n (x, y) is defined as follows:…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Physarum polycephalus has been shown to rely on reactive navigation to explore the environment and search for multiple food. A number of foraging models have been proposed [24][25][26][27][28][29]. Related researches illustrate that Physarum has an ability to solve maze navigation [30] and graph theory problems [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Related researches illustrate that Physarum has an ability to solve maze navigation [30] and graph theory problems [31][32][33][34]. e contributions of this work are as follows: (1) we propose a novel multi-source search strategy named PS by modifying the grown rule of the Physarum foraging model in [28]; and (2) an extension algorithm named PDS is developed, considering the decision-making of each source and obstacle avoidance. In simulations, the PDS has the best performance compared to other three algorithms: Sweep, Yuragi-based strategy, and PS.…”

Section: Introductionmentioning

confidence: 99%

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A Physarum-Inspired Decision-Making Strategy for Multisource Task Searching of Mobile Robots

Jiang

2020

Complexity

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In the real world, there are many different kinds of sources, such as light, sound, and gas, distributed randomly over an area. Source search can be carried out by robotic system in applications. However, for a single robot, the multisource search has been receiving relatively little attention compared to single-source search. For multisource task searching, a single robot has a high travel cost and is easy to trap a source which has been located before. In order to overcome these shortages, two multisource search algorithms inspired by the foraging behavior of Physarum polycephalum are proposed in this paper. First, a Physarum-inspired Strategy (PS) is designed based on the gradient climbing characteristic of Physarum polycephalum during foraging. The PS is simple and effective to let a mobile robot traverse all sources. Then, an extension algorithm named Physarum-inspired Decision-making Strategy (PDS) is proposed based on PS. Therein the synthetical field gradient model is established by introducing decision-making factor to obtain more accurate gradient information estimation. The PDS also introduces an obstacle avoidance model. Various simulation results obtained in the multisource environments show that the performance of PDS is better than other algorithms.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Physarum-Inspired Decision-Making Strategy for Multisource Task Searching of Mobile Robots

Jiang

2020

Complexity

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“…The Physarum machine can construct spanning trees [25] and solve mazes [45]. Taking advantage of the idea that food sources play a major role in inducing the movement direction of diffusion waves in the Oregonator model, Wu et al [46] have proposed a new model based on the gradient of chemical nutrient-particles field, which can imitate Physarum to construct the spanning trees on a nutrient-poor substrate.…”

Section: B Physarum Foraging Modelsmentioning

confidence: 99%

Simulating Transport Networks With a <italic>Physarum</italic> Foraging Model

2019

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Designing effective transport networks can be considered as one of the most debated problems in the area of computational intelligence. Some nature-inspired algorithms have shown excellent abilities in the adaptive network construction. In this aspect, a unique creature, called Physarum, has exhibited the computing capacity to optimize protoplasmic networks connecting distributed food sources. This inspires our work to design a Physarum foraging platform for constructing transport networks. Specifically, the traditional Physarum foraging model is adapted to construct transport networks in China. In order to get close to the real scenario, practical data are collected to build the environment of the Physarum foraging model and the structure of real transport networks. Some measurements in the domain of complex networks, such as average path length, network efficiency, topology robustness, and functional robustness, are used for performance comparison. The experimental results demonstrate that Physarum foraging models excel in constructing highly efficient and robust networks, which can be utilized for directing the design of transport networks in the real world. INDEX TERMS Physarum polycephalum, Physarum foraging model, network analysis, transport network design.

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“…Inoculating at a map substrate in which food sources are arranged in major cities, Physarum can design a transport network, which has the comparable efficiency to the real-world infrastructure network (Tero et al 2010;Adamatzky 2012). The amazing observation inspires an innovative spark based on biological and physical theories to uncover the key of intelligence hidden in Physarum over the last few years (Tero et al 2007;Adamatzky 2007Adamatzky , 2012Gunji et al 2008;Jones 2010;Wu et al 2015;Zeitoun et al 2012;Alim et al 2013). Tero et al have proposed a mathematical model combining Hagen-Poiseuille Law and Kirchhoff Law to explain the path-finding and the maze-solving process of Physarum (Tero et al 2007(Tero et al , 2006.…”

Section: Introductionmentioning

confidence: 99%

A new multi-agent system to simulate the foraging behaviors of Physarum

et al. 2015

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Physarum Polycephalum is a unicellular and multi-headed slime mold, which can form high efficient networks connecting spatially separated food sources in the process of foraging. Such adaptive networks exhibit a unique characteristic in which network length and fault tolerance are appropriately balanced. Based on the biological observations, the foraging process of Physarum demonstrates two self-organized behaviors, i.e., search and contraction. In this paper, these two behaviors are captured in a multi-agent system. Two types of agents and three transition rules are designed to imitate the search and the contraction behaviors of Physarum based on the necessary and the sufficient conditions of a self-organized computational system. Some simulations of foraging process are used to investigate the characteristics of our system. Experimental results show that our system can autonomously search for food sources and then converge to a stable solution, which replicates the foraging process of Physarum. Specially, a case study of maze problem is used to estimate the path-finding ability of the foraging behaviors of Physarum. What's more, the model inspired by the foraging behaviors of Physarum is proposed to optimize meta-heuristic algorithms for solving optimization problems. Through comparing the optimized algorithms and the corresponding traditional algorithms, we have found that the optimization strategies have a higher computational performance than their corresponding traditional algorithms, which further justifies that the foraging behaviors of Physarum have a higher computational ability.Keywords Physarum Polycephalum Á Multi-agent system Á Self-organized computational system Á Maze

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A new model to imitate the foraging behavior of Physarum polycephalum on a nutrient-poor substrate

Cited by 10 publications

References 22 publications

A Physarum-Inspired Decision-Making Strategy for Multisource Task Searching of Mobile Robots

A Physarum-Inspired Decision-Making Strategy for Multisource Task Searching of Mobile Robots

Simulating Transport Networks With a <italic>Physarum</italic> Foraging Model

A new multi-agent system to simulate the foraging behaviors of Physarum

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