Membrane Computing Research Articles

A membrane computing framework for social navigation in robotics

A mobile robot acting in a human environment should follow social conventions, keeping safety distances and navigating at moderate speeds, in order to respect people in its surroundings and avoid obstacles in real-time. The problem is more complex in differential-drive wheeled robots, with trajectories constrained by nonholonomic and kinematics restrictions. It is an NP-hard problem widely studied in the literature, combining disciplines such as Psychology, Mathematics, Computer Science and Engineering. In this work, we propose a novel solution based on Membrane Computing, Social Force Model and Dynamic Window Approach Algorithm. The resulting model is able to compute, in logarithmic time, the best motion command for the robot, given its current state, considering the surrounding people and obstacles. The model is compatible with other membrane computing models for robotics and suitable for an implementation on parallel hardware. Finally, a visual simulator was implemented in ROS and C++ for validation and testing.

Replicating the MAP Kinase Cascade in Membrane Computing

Membrane Computing (MC) is defined as one of the main areas in computer sciences; MC has the aim of discovering novel computational models from studying biological cells, specifically the cellular membranes. Mitogen-Activated Protein Kinases (MAPK) cascade was the subject of research in the areas of modeling and simulation. Various software tools such as Performance Evaluation Process Algebra (PEPA) have been used to solve the MAPK cascade for the purpose of improving the effectiveness of signaling. In this study, the MAPK cascade is modeled by using MC. The models of Membrane Computing could be totally fully utilized by applying parallel computing platforms. P-Lingua can be defined as a programming language for MeCoSim and MC, where MC simulators are used to model and simulate MAPK. P-Lingua will be applied to structure, develop and examine the implementation of MAPK cascades in membrane computing. MeCoSim supports charts, outputs, and inputs which have been adapted to MC. The simulation results have been put to comparison with PEPA model. The results indicate that MC improves the MAPK implementation compared to PEPA. This study showed that MC, with its biological characteristics, could improve the implementation regarding biological processes including MAPK.

Factual power loss reduction by enriched black hole algorithm

This paper presents enriched black hole algorithm (EBHA) for solving optimal reactive power problem. In this work black hole algorithm based on membrane computing is projected. In black hole algorithm evolution of the population is through pushing the candidates in the course of the most excellent candidate in iterations and black hole which swap with those in the search space. Membrane computing is also branded as P system and it has multisets of objects with evolution rules in the membrane structure. Membrane structure is alike ingrained tree of section that demarcate the areas, and root is labelled as skin. Chemical substances (multisets of objects) are there inside the section (membranes) of a cell and the chemical reactions (evolution rules) that take place within the cell. Proposed enriched black hole algorithm (EBHA) has been evaluated in IEEE 14,300 bus test system. Loss reduction achieved.

Numerical Spiking Neural P Systems.

Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire behavior of neurons and the symbolic representation of information, SN P systems are incompatible with the gradient descent-based training algorithms, such as the backpropagation algorithm, and lack the capability of processing the numerical representation of information. In this work, motivated by the numerical nature of numerical P (NP) systems in the area of membrane computing, a novel class of SN P systems is proposed, called numerical SN P (NSN P) systems. More precisely, information is encoded by the values of variables, and the integrate-and-fire way of neurons and the distribution of produced values are described by continuous production functions. The computation power of NSN P systems is investigated. We prove that NSN P is Turing universal as number generating devices, where the production functions in each neuron are linear functions, each involving at most one variable; as number accepting devices, NSN P systems are proved to be universal as well, even if each neuron contains only one production function. These results show that even if a single neuron is simple in the sense that it contains one or two production functions and the production functions in each neuron are linear functions with one variable, a network of simple neurons are still computationally powerful. With the powerful computation power and the characteristic of continuous production functions, developing learning algorithms for NSN P systems is potentially exploitable.

Tuning Frontiers of Efficiency in Tissue P Systems with Evolutional Communication Rules

Over the last few years, a new methodology to address the P versus NP problem has been developed, based on searching for borderlines between the nonefficiency of computing models (only problems in class P can be solved in polynomial time) and the presumed efficiency (ability to solve NP-complete problems in polynomial time). These borderlines can be seen as frontiers of efficiency, which are crucial in this methodology. “Translating,” in some sense, an efficient solution in a presumably efficient model to an efficient solution in a nonefficient model would give an affirmative answer to problem P versus NP. In the framework of Membrane Computing, the key of this approach is to detect the syntactic or semantic ingredients that are needed to pass from a nonefficient class of membrane systems to a presumably efficient one. This paper deals with tissue P systems with communication rules of type symport/antiport allowing the evolution of the objects triggering the rules. In previous works, frontiers of efficiency were found in these kinds of membrane systems both with division rules and with separation rules. However, since they were not optimal, it is interesting to refine these frontiers. In this work, optimal frontiers of the efficiency are obtained in terms of the total number of objects involved in the communication rules used for that kind of membrane systems. These optimizations could be easier to translate, if possible, to efficient solutions in a nonefficient model.

Spiking Neural Membrane Computing Models

As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved.

Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

BBO is one of the new metaheuristic optimization algorithms, which is based on the science of biogeography. It can be used to solve optimization problems through the migration and drift of species between habitats. Many improved BBO algorithms have been proposed, but there were still many shortcomings in global optimization, convergence speed, and algorithm complexity. In response to the above problems, this paper proposes an improved BBO algorithm (DCGBBO) by hierarchical tissue-like P system with triggering ablation rules. Membrane computing is a branch of natural computing that aims to abstract computational models (P system) from the structure and function of biological cells and from the collaboration of cell groups such as organs and tissues. In this paper, firstly, a dynamic crossover migration operator is generated to improve the global search ability and also increase the species diversity. Secondly, a dynamic Gaussian mutation operator is introduced to speed up convergence and improve local search capabilities. To guarantee the correctness and feasibility of the mutation, a unified maximum mutation rate is designed. Finally, a hierarchical tissue-like P system with triggering ablation rules is combined with the DCGBBO algorithm, making use of evolution rules and communication rules to achieve migration and mutation of solutions and reduce computational complexity. In the experiments, eight classic benchmark functions and CEC 2017 benchmark functions are applied to demonstrate the effect of our algorithm. We apply the proposed algorithm to segment four colour pictures, and the results proved to be better compared to other algorithms.

A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image

Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is considered as the main theme in HSI classification to reduce the data dimension, and it is a combinatorial optimization problem and difficult to be completely solved by previous techniques. Whale Optimization Algorithm (WOA) is a newly proposed swarm intelligence algorithm that imitates the predatory strategy of humpback whales, and membrane computing is able to decompose the band information into a series of elementary membranes that decreases the coding length. In addition, Support Vector Machine (SVM) combined with wavelet kernel is adapted to HSI datasets with high dimension and small samples, ensemble learning is an effective tool that synthesizes multiple sub-classifiers to solve the same problem and obtains accurate category label for each sample. In the paper, a band selection approach based on wavelet SVM (WSVM) ensemble model and membrane WOA (MWOA) is proposed, experimental results indicate that the proposed HSI classification technique is superior to other corresponding and newly proposed methods, achieves the optimal band subset with a fast convergence speed, and the overall classification accuracy has reached 93% for HSIs.

An In Vivo Proposal of Cell Computing Inspired by Membrane Computing

Intractable problems are challenging and not uncommon in Computer Science. The computing generation we are living in forces us to look for an alternative way of computing, as current computers are facing limitations when dealing with complex problems and bigger input data. Physics and Biology offer great alternatives to solve these problems that traditional computers cannot. Models like Quantum Computing and cell computing are emerging as possible solutions to the current problems the conventional computers are facing. This proposal describes an in vivo framework inspired by membrane computing and based on alternative computational frameworks that have been proven to be theoretically correct such as chemical reaction series. The abilities of a cell as a computational unit make this proposal a starting point in the creation of feasible potential frameworks to enhance the performance of applications in different disciplines such as Biology, BioMedicine, Computer networks, and Social Sciences, by accelerating drastically the way information is processed by conventional architectures and possibly achieving results that presently are not possible due to the limitations of the current computing paradigm. This paper introduces an in vivo solution that uses the principles of membrane computing and it can produce non-deterministic outputs.

A Survey of Nature-Inspired Computing

Nature-inspired computing is a type of human-designed computing motivated by nature, which is based on the employ of paradigms, mechanisms, and principles underlying natural systems. In this article, a versatile and vigorous bio-inspired branch of natural computing, named membrane computing is discussed. This computing paradigm is aroused by the internal membrane function and the structure of biological cells. We first introduce some basic concepts and formalisms of membrane computing, and then some basic types or variants of P systems (also named membrane systems ) are presented. The state-of-the-art computability theory and a pioneering computational complexity theory are presented with P system frameworks and numerous solutions to hard computational problems (especially NP -complete problems) via P systems with membrane division are reported. Finally, a number of applications and open problems of P systems are briefly described.

Delayed Spiking Neural P Systems with Scheduled Rules

Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural‐like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved.

P Systems Implementation: A Model of Computing for Biological Mitochondrial Rules using Object Oriented Programming

Membrane computing is a computational framework that depends on the behavior and structure of living cells. P systems are arising from the biological processes which occur in the living cells’ organelles in a non-deterministic and maximally parallel manner. This paper aims to build a powerful computational model that combines the rules of active and mobile membranes, called Mutual Dynamic Membranes (MDM). The proposed model will describe the biological mechanisms of the metabolic regulation of mitochondrial dynamics made by mitochondrial membranes. The behaviors of the proposed model regulate the mitochondrial fusion and fission processes based on the combination of P systems variants. The combination of different variants in our computational model and their high parallelism lead to provide the possibility for solving problems that belong to NP-complete classes in polynomial time in a more efficient way than other conventional methods. To evaluate this model, it was applied to solve the SAT problem and calculate a set of computational complexity results that approved the quality of our model. Another contribution of this paper, the biological models of mitochondrial is presented in the formal class relationship diagrams were designed and illustrated using Unified Modeling Language (UML). This mechanism will be used to define a new specification of membrane processes into Object-Oriented Programming (OOP) to add the functionality of a common programming methodology to solve a large category of NP-hard problems as an interesting initiative of future research.

IPSOMC: An Improved Particle Swarm Optimization and Membrane Computing based Algorithm for Cloud Computing

IPSOMC: An Improved Particle Swarm Optimization and Membrane Computing based Algorithm for Cloud Computing

Membrane-based models for service selection in cloud

Cloud service selection is one of the prime areas of research within the ambit of cloud computing, which has gained wide attention in the recent past. The service selection algorithm primarily involves selecting the best service from a set of available services, based on Quality of Service (QoS) attributes. The QoS attributes are the parameters which allow the users to ascertain the actual quality of the service, often quantitatively. Over the years, there have been several methods designed for service selection in the cloud that are primarily sequential, with many being sensitive to changes. Thus, the aim is to propose multiple robust and parallel models for cloud service selection. The proposed models are designed using Membrane Computing paradigm, which is an inherently parallel computing model, combined with the Improved Technique for Order of Preference by Similarity to Ideal Solution (ITOPSIS), a popular Multi-Criteria Decision Making Method. Two methods based on a tactical amalgamation of ITOPSIS and Enzymatic Numerical P System (A membrane computing device variant) structure are proposed here. The proposed parallel models are implemented, tested, and the obtained results are analyzed. The results indicate one model to be robust (less sensitive) and the other to be moderately sensitive.

A Bio-Inspired Model of Picture Array Generating P System with Restricted Insertion Rules

In the bio-inspired area of membrane computing, a novel computing model with a generic name of P system was introduced around the year 2000. Among its several variants, string or array language generating P systems involving rewriting rules have been considered. A new picture array model of array generating P system with a restricted type of picture insertion rules and picture array objects in its regions, is introduced here. The generative power of such a system is investigated by comparing with the generative power of certain related picture array grammar models introduced and studied in two-dimensional picture language theory. It is shown that this new model of array P system can generate picture array languages which cannot be generated by many other array grammar models. The theoretical model developed is for handling the application problem of generation of patterns encoded as picture arrays over a finite set of symbols. As an application, certain floor-design patterns are generated using such an array P system.

Seeking computational efficiency boundaries: the Păun’s conjecture

In 2005, Gh. Păun raised an interesting question concerning the role of electrical charges in P systems with active membranes from a complexity point of view. Specifically, he formulated a question about the computational efficiency of polarizationless P systems with dissolution rules and division rules only for elementary membranes. Several approaches have been carried out, and some partial answers have been given. This is probably the most important open problem in computational complexity theory in the framework of Membrane Computing. The study of the efficiency of membrane systems has been a very fruitful area, providing not only the above-stated partial answers, but also several frontiers of efficiency to tackle the P vs NP problem. In this work, a survey on classical and current results on complexity aspects is given, emphasizing on the frontiers of efficiency and the ingredients taken into account for each of them.

Morphogenetic systems: Models and experiments

M systems are mathematical models of morphogenesis developed to gain insights into its relations to phenomena such as self-assembly, self-controlled growth, homeostasis, self-healing and self-reproduction, in both natural and artificial systems. M systems rely on basic principles of membrane computing and self-assembly, as well as explicit emphasis on geometrical structures (location and shape) in 2D, 3D or higher dimensional Euclidean spaces. They can be used for principled studies of these phenomena, both theoretically and experimentally, at a computational level abstracted from their detailed implementation. In particular, they afford 2D and 3D models to explore biological morphogenetic processes. Theoretical studies have shown that M systems are powerful tools (e.g., computational universal, i.e. can become as complex as any computer program) and their parallelism allows for trading space for time in solving efficiently problems considered infeasible on conventional computers (NP-hard problems). In addition, they can also exhibit properties such as robustness to injuries and degrees of self-healing.This paper focuses on the experimental side of M systems. To this end, we have developed a high-level morphogenetic simulator, Cytos, to implement and visualize M systems in silico in order to verify theoretical results and facilitate research in M systems. We summarize the software package and make a brief comparison with some other simulators of membrane systems. The core of the article is a description of a range of experiments inspired by aspects of morphogenesis in both prokaryotic and eukaryotic cells. The experiments explore the regulatory role of the septum and of the cytoskeleton in cell fission, the robustness of cell models against injuries, and, finally, the impact of changing nutrient concentration on population growth.

Proof techniques in Membrane Computing

From the creation of the field of Membrane Computing in 1998, several research lines have been opened. On the one hand, theoretical questions like the computational power and the computational efficiency of P systems have been studied. In this sense, several techniques to demonstrate the ability of these systems to provide solutions to computational problems have been explored. The study of efficient (polynomial-time) solutions to presumably hard problems for finding thin frontiers of efficiency is a very active area. On the other hand, several applications in biology, ecology, economy, robotics and fault diagnosis, among others, have been investigated. Real systems with some characteristics seem to be easy to model with membrane systems due to their behaviour. In this work, a survey of the theoretical part will be given, explaining techniques both in the field of computability theory and in the field of computational complexity theory.

A Review of Membrane Computing Models for Complex Ecosystems and a Case Study on a Complex Giant Panda System

Ecosystem modelling based on membrane computing is emerging as a powerful way to study the dynamics of (real) ecological populations. These models, providing distributed parallel devices, have shown a great potential to imitate the rich features observed in the behaviour of species and their interactions and key elements to understand and model ecosystems. Compared with differential equations, membrane computing models, also known as P systems, can model more complex biological phenomena due to their modularity and their ability to enclose the evolution of different environments and simulate, in parallel, different interrelated processes. In this paper, a comprehensive survey of membrane computing models for ecosystems is given, taking a giant panda ecosystem as an example to assess the model performance. This work aims at modelling a number of species using P systems with different membrane structure types to predict the number of individuals depending on parameters such as reproductive rate, mortality rate, and involving processes as rescue or release. Firstly, the computing models are introduced conceptually, describing the main elements constituting the syntax of these systems and explaining the semantics of the rules involved. Next, various modelled species (including endangered animals, plants, and bacteria) are summarized, and some computer tools are presented. Then, a discussion follows on the use of P systems for ecosystem modelling. Finally, a case study on giant pandas in Chengdu Base is analysed, concluding that the study in this field by using PDP systems can provide a valuable tool to deepen into the knowledge about the evolution of the population. This could ultimately help in the decision-making processes of the managers of the ecosystem to increase the species diversity and modify the adaptability. Besides, the impacts of natural disasters on the population dynamics of the species should also be considered. The analysis performed throughout the paper has taken into consideration this fact in order to increase the reliability of the prospects making use of the models designed.

A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit

Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism to compute general-purpose computations. Previous studies mapped one membrane to one thread block on GPU. This is disadvantageous given that when the quantity of objects for each membrane is small, the quantity of active thread will also be small, thereby decreasing performance. While each membrane is designated to one thread block, the communication between thread blocks is needed for executing the communication between membranes. Communication between thread blocks is a time-consuming process. Previous approaches have also not addressed the issue of GPU occupancy. This study presents a classification algorithm to manage dependent objects and membranes based on the communication rate associated with the defined weighted network and assign them to sub-matrices. Thus, dependent objects and membranes are allocated to the same threads and thread blocks, thereby decreasing communication between threads and thread blocks and allowing GPUs to maintain the highest occupancy possible. The experimental results indicate that for 48 objects per membrane, the algorithm facilitates a 93-fold increase in processing speed compared to a 1.6-fold increase with previous algorithms.