An Enhanced Multi-Agent System with Evolution Mechanism to Approximate Physarum Transport Networks

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

doi:10.1007/978-3-642-35101-3_3

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In this model, the structure of the Physarum network is indicated by the collective pattern of the positions of agents, and the protoplasmic flow is represented by the collective movement of agents. Furthermore, Wu et al (2012) improved the initial multi-agent model by adding a memory module to each agent [101]. This improved model is more flexible and adaptive, and it approximates the behaviours of Physarum more closely.…”

Section: The Multi-agent Modelmentioning

confidence: 99%

A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications

Awad¹,

Lusseau²,

Coghill³

2021

Preprint

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In recent years, research on Physarum polycephalum has become more popular after Nakagaki et al. (2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze. Subsequent researches have confirmed the ability of Physarum-inspired algorithms to solve a wide range of NP-hard problems. This review will through light on recent Physarum polycephalum biological aspects, mathematical models, and Physarum bio-inspired algorithms and their applications. Further, we have presented our new model to simulate Physarum in competition, where multiple Physarum interact with each other and with their environments. The bio-inspired Physarum in competition algorithms proved to have great potentials for future research.

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Section: The Multi-agent Modelmentioning

confidence: 99%

A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications

Awad¹,

Lusseau²,

Coghill³

2021

Preprint

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show abstract

“…The experimental results illustrate that simple local behaviors, according to the characteristics of chemotaxis, can guide the agent population and generate more complex and dynamic transport networks [38]. Later, Liu et al [36] and Wu et al [47] have improved the multi-agent model by optimizing the structure of each agent and designing two types of agents to simulate the search and the contraction behaviors of Physarum respectively.…”

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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“…With the system running, the initial chaotic formation of agents can converge to a stable state and form a network connecting all data points (i.e., food sources). For enhancing the self-organization process in Jones's model, Wu et al have proposed a multiagent system with self-adaptable population by optimizing agent architecture and adding evolution mechanism (Wu et al 2012). Simulation results of experiments show that Wu's system can automatically adjust the agent population, maintain its homeostasis in the macroscopic formation.…”

Section: Introductionmentioning

confidence: 98%

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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An Enhanced Multi-Agent System with Evolution Mechanism to Approximate Physarum Transport Networks

Cited by 5 publications

References 14 publications

A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications

A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications

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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