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Andrew Adamatzky - One of the best experts on this subject based on the ideXlab platform.
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structural machines and slime mould computation
International Journal of General Systems, 2017Co-Authors: Mark Burgin, Andrew AdamatzkyAbstract:AbstractA Physarum machine is a programmable amorphous biological computer experimentally implemented in the vegetative state of true slime mould Physarum polycephalum. It comprises an amorphous yellowish mass with networks of protoplasmic veins, programmed by spatial configurations of attracting and repelling gradients. The goal of this paper to advance formalism of Physarum machines providing theoretical tools for exploration of possibilities of these machines and extension of their applications. To achieve this goal, we introduce structural machines and study their properties.
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Physarum solver a bio inspired method for sustainable supply chain network design problem
Annals of Operations Research, 2017Co-Authors: Andrew Adamatzky, Xiaoge Zhang, Felix T S Chan, Sankaran Mahadevan, Yong DengAbstract:A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples.
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an intelligent Physarum solver for supply chain network design under profit maximization and oligopolistic competition
International Journal of Production Research, 2017Co-Authors: Xiaoge Zhang, Andrew Adamatzky, Felix T S Chan, Sankaran Mahadevan, Zili Zhang, Hai Yang, Yong DengAbstract:We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a sup...
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thirty seven things to do with live slime mould
2017Co-Authors: Andrew AdamatzkyAbstract:Slime mould Physarum polycephalum is a large single cell capable for distributed sensing, concurrent information processing, parallel computation and decentralised actuation. The ease of culturing and experimenting with Physarum makes this slime mould an ideal substrate for real-world implementations of unconventional sensing and computing devices. In the last decade the Physarum became a swiss knife of the unconventional computing: give the slime mould a problem it will solve it. We provide a concise summary of what exact computing and sensing operations are implemented with live slime mould. The Physarum devices discussed range from morphological processors for computational geometry to experimental archeology tools, from self-routing wires to memristors, from devices approximating a shortest path to analog physical models of space exploration.
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on coupled oscillator dynamics and incident behaviour patterns in slime mould Physarum polycephalum emergence of wave packets global streaming clock frequencies and anticipation of periodic stimuli
International Journal of Parallel Emergent and Distributed Systems, 2017Co-Authors: Richard Mayne, Jeff Jones, Ella Gale, Andrew AdamatzkyAbstract:The Physarum polycephalum actin network in a plasmodial tubule. SiR-actin staining, scale bar 200 μm.
Yong Deng - One of the best experts on this subject based on the ideXlab platform.
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Physarum solver a bio inspired method for sustainable supply chain network design problem
Annals of Operations Research, 2017Co-Authors: Andrew Adamatzky, Xiaoge Zhang, Felix T S Chan, Sankaran Mahadevan, Yong DengAbstract:A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples.
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an intelligent Physarum solver for supply chain network design under profit maximization and oligopolistic competition
International Journal of Production Research, 2017Co-Authors: Xiaoge Zhang, Andrew Adamatzky, Felix T S Chan, Sankaran Mahadevan, Zili Zhang, Hai Yang, Yong DengAbstract:We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a sup...
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an improved Physarum polycephalum algorithm for the shortest path problem
The Scientific World Journal, 2014Co-Authors: Xiaoge Zhang, Andrew Adamatzky, Felix T S Chan, Sankaran Mahadevan, Qing Wang, Yong DengAbstract:Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm.
Xiaoge Zhang - One of the best experts on this subject based on the ideXlab platform.
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an accelerated Physarum solver for network optimization
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Cai Gao, Xiaoge Zhang, Zhiying Yue, Daijun WeiAbstract:As a novel computational paradigm, Physarum solver has received increasing attention from the researchers in tackling a plethora of network optimization problems. However, the convergence of Physarum solver is grounded by solving a system of linear equations iteratively, which often leads to low computational performance. Two factors have been highlighted along the process: 1) high time complexity in solving the system of linear equations and 2) extensive iterations required for convergence. Thus, Physarum solver has been largely restricted by its unsatisfactory computational performance. In this paper, we aim to address these two issues by developing two enhancement strategies: 1) pruning inactive nodes and 2) terminating Physarum solver in advance. First, extensive nodes and edges become and stay inactive after a few iterations in identifying the shortest path. Removing these inactive nodes and edges significantly decreases the graph size, thereby reducing computational complexity. Second, we define a transition phase for edges. All of the paths experiencing such a transition phase are dynamically aggregated to form a set of near-optimal paths among which the optimal path is included. Depth-first search is then leveraged to identify the optimal path from the near-optimal paths set. Earlier termination of Physarum solver saves considerable iterations while guaranteeing the optimality of the found solution. Empirically, 20 randomly generated sparse and complete graphs with network sizes ranging from 50 to 2000 as well as two real-world traffic networks are used to compare the performance of accelerated Physarum solver to the other two state-of-the-art algorithms.
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a bio inspired approach to traffic network equilibrium assignment problem
IEEE Transactions on Systems Man and Cybernetics, 2018Co-Authors: Xiaoge Zhang, Sankaran MahadevanAbstract:Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes. At last, we demonstrate that the Physarum solver converges to the user-optimized (Wardrop) equilibrium by dynamically updating the costs of links in the network. In addition, convergence of the developed algorithm is proved. Numerical examples are used to demonstrate the efficiency of the proposed algorithm. The superiority of the proposed algorithm is demonstrated in comparison with several other algorithms, including the Frank-Wolfe algorithm, conjugate Frank-Wolfe algorithm, biconjugate Frank-Wolfe algorithm, and gradient projection algorithm.
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Physarum solver a bio inspired method for sustainable supply chain network design problem
Annals of Operations Research, 2017Co-Authors: Andrew Adamatzky, Xiaoge Zhang, Felix T S Chan, Sankaran Mahadevan, Yong DengAbstract:A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples.
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an intelligent Physarum solver for supply chain network design under profit maximization and oligopolistic competition
International Journal of Production Research, 2017Co-Authors: Xiaoge Zhang, Andrew Adamatzky, Felix T S Chan, Sankaran Mahadevan, Zili Zhang, Hai Yang, Yong DengAbstract:We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a sup...
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an improved Physarum polycephalum algorithm for the shortest path problem
The Scientific World Journal, 2014Co-Authors: Xiaoge Zhang, Andrew Adamatzky, Felix T S Chan, Sankaran Mahadevan, Qing Wang, Yong DengAbstract:Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm.
Toshiyuki Nakagaki - One of the best experts on this subject based on the ideXlab platform.
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does being multi headed make you better at solving problems a survey of Physarum based models and computations
Physics of Life Reviews, 2019Co-Authors: Chao Gao, Zili Zhang, Chen Liu, Daniel Schenz, Marko Jusup, Zhen Wang, Madeleine Beekman, Toshiyuki NakagakiAbstract:Physarum polycephalum, a single-celled, multinucleate slime mould, is a seemingly simple organism, yet it exhibits quasi-intelligent behaviour during extension, foraging, and as it adapts to dynamic environments. For these reasons, Physarum is an attractive target for modelling with the underlying goal to uncover the physiological mechanisms behind the exhibited quasi-intelligence and/or to devise novel algorithms for solving complex computational problems. The recent increase in modelling studies on Physarum has prompted us to review the latest developments in this field in the context of modelling and computing alike. Specifically, we cover models based on (i) morphology, (ii) taxis, and (iii) positive feedback dynamics found in top-down and bottom-up modelling techniques. We also survey the application of each of these core features of Physarum to solving difficult computational problems with real-world applications. Finally, we highlight some open problems in the field and present directions for future research.
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Physarum inspires research beyond biomimetic algorithms reply to comments on does being multi headed make you better at solving problems
Physics of Life Reviews, 2019Co-Authors: Chao Gao, Zili Zhang, Chen Liu, Daniel Schenz, Marko Jusup, Zhen Wang, Madeleine Beekman, Toshiyuki NakagakiAbstract:We look at a recent expansion of Physarum research from inspiring biomimetic algorithms to serving as a model organism in the evolutionary study of perception, memory, learning, and decision making.
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convergence properties for the Physarum solver
arXiv: Optimization and Control, 2011Co-Authors: Kentaro Ito, Toshiyuki Nakagaki, Anders Johansson, Atsushi TeroAbstract:The Physarum solver [31, 18] is an intuitive mechanism for solving optimisation problems. The solver is based on the idea of a current reinforced electrical network, whereby the conductivity �(t) 2 R E is reinforced by the current or flow, �(t) 2 R E. In this paper, we show how the Physarum solver obtains the solution to the linear transshipment problem on a digraph G = (V;E). We prove that the current �(t) and �(t) converge with
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Flow-network adaptation in Physarum amoebae
Theory in Biosciences, 2008Co-Authors: Atsushi Tero, Kenji Yumiki, Ryo Kobayashi, Tetsu Saigusa, Toshiyuki NakagakiAbstract:Understanding how biological systems solve problems could aid the design of novel computational methods. Information processing in unicellular eukaryotes is of particular interest, as these organisms have survived for more than a billion years using a simple system. The large amoeboid plasmodium of Physarum is able to solve a maze and to connect multiple food locations via a smart network. This study examined how Physarum amoebae compute these solutions. The mechanism involves the adaptation of the tubular body, which appears to be similar to a network, based on cell dynamics. Our model describes how the network of tubes expands and contracts depending on the flux of protoplasmic streaming, and reproduces experimental observations of the behavior of the organism. The proposed algorithm based on Physarum is simple and powerful.
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intelligent behaviors of amoeboid movement based on complex dynamics of soft matter
Soft Matter, 2008Co-Authors: Toshiyuki Nakagaki, Robert D GuyAbstract:We review how soft matter is self-organized to perform information processing at the cell level by examining the model organism Physarum plasmodium. The amoeboid organism, Physarum polycephalum, in the class of true slime molds, exhibits the intelligent behavior of foraging in complex situations. When placed in a maze with food sources at two exits, the organism develops tubular structures with its body which connect the food sources along the shortest path so that the rates of nutrient absorption and intracellular communication are maximized. This intelligent behavior results from the organism's control of a dynamic network through which mechanical and chemical information is transmitted. We review experimental studies that explore the development and adaptation of structures that make up the network. Recently a model of the dynamic network has been developed, and we review the formulation of this model and present some key results. The model captures the dynamics of existing networks, but it does not answer the question of how such networks form initially. To address the development of cell shape, we review existing mechanochemical models of the protoplasm of Physarum, present more general models of motile cells, and discuss how to adapt existing models to explore the development of intelligent networks in Physarum.
Steinar Johansen - One of the best experts on this subject based on the ideXlab platform.
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polyphyletic origin of the genus Physarum physarales myxomycetes revealed by nuclear rdna mini chromosome analysis and group i intron synapomorphy
BMC Evolutionary Biology, 2012Co-Authors: Satish C R Nandipati, Kari Haugli, Daghugo Coucheron, Edward F Haskins, Steinar JohansenAbstract:Background: Physarales represents the largest taxonomic order among the plasmodial slime molds (myxomycetes). Physarales is of particular interest since the two best-studied myxomycete species, Physarum polycephalum and Didymium iridis, belong to this order and are currently subjected to whole genome and transcriptome analyses. Here we report molecular phylogeny based on ribosomal DNA (rDNA) sequences that includes 57 Physarales isolates. Results: The Physarales nuclear rDNA sequences were found to be loaded with 222 autocatalytic group I introns, which may complicate correct alignments and subsequent phylogenetic tree constructions. Phylogenetic analysis of rDNA sequences depleted of introns confirmed monophyly of the Physarales families Didymiaceae and Physaraceae. Whereas good correlation was noted between phylogeny and taxonomy among the Didymiaceae isolates, significant deviations were seen in Physaraceae. The largest genus, Physarum, was found to be polyphyletic consisting of at least three well supported clades. A synapomorphy, located at the highly conserved G-binding site of L2449 group I intron ribozymes further supported the Physarum clades. Conclusions: Our results provide molecular relationship of Physarales genera, species, and isolates. This information is important in further interpretations of comparative genomics nd transcriptomics. In addition, the result supports a polyphyletic origin of the genus Physarum and calls for a reevaluation of current taxonomy.