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• P-Lingua is a well-known programming language for membrane systems. • P-Lingua 5 features customized P system variants definitions, including semantics. • Derivation modes and simulation directives are now described in P-Lingua 5 code. • P-Lingua 5 software toolkit (compiler and simulator) is backward-compatible. Membrane computing is a massively parallel and non-deterministic bioinspired computing paradigm whose models are called P systems. Validating and testing such models is a challenge which is being overcome by developing simulators. Regardless of their heterogeneity, such simulators require to read and interpret the models to be simulated. To this end, P-Lingua is a high-level P system definition language which has been widely used in the last decade. The P-Lingua ecosystem includes not only the language, but also libraries and software tools for parsing and simulating membrane computing models. Each version of P-Lingua supported new types or variants of P systems. This leads to a shortcoming: Only a predefined list of variants can be used, thus making it difficult for researchers to study custom ones. Moreover, derivation modes cannot be user-defined, i.e, the way in which P system computations should be generated is determined by the simulation algorithm in the source code. The main contribution of this paper is a completely new design of the P-Lingua language, called P-Lingua 5, in which the user can define custom variants and derivation modes, among other improvements such as including procedural programming and simulation directives. It is worth mentioning that it has backward-compatibility with previous versions of the language. A completely new set of command-line tools is provided for parsing and simulating P-Lingua 5 files. Finally, several examples are included in this paper covering the most common P system types. [ABSTRACT FROM AUTHOR]

Membrane computing is an unconventional computing paradigm that has gained much attention in recent decades because of its massively parallel character and its usefulness to build models of complex systems. However, until now, there was no generic hardware implementation of P systems. Computational frameworks to execute P systems up to this day rely on the simulation of the parallel working mechanisms of P systems by inherently sequential algorithms. Such algorithms can then be implemented as is or can be parallelized, up to a certain point, to run on parallel computers. However, this is not as efficient as a dedicated parallel hardware implementation. There have been ad hoc implementations of particular P systems for parallel hardware, but they lack to be problem-generic or they are not scalable enough to implement large P systems. In this paper, a first intrinsically parallel hardware architecture to implement generic P system models is introduced. It is designed to be straightforwardly implemented in programmable logic circuits like FPGAs. The feasibility and correct execution of our architecture has been verified by means of a simulator, and several simulation results for different P system examples have been analysed to foresee the pros and cons of this design. [ABSTRACT FROM AUTHOR]

This paper focuses on the unsupervised learning problem within membrane computing, and proposes an innovative solution inspired by membrane computing techniques, the fuzzy membrane clustering algorithm. An evolution–communication P system with nested membrane structure is the core component of the algorithm. The feasible cluster centers are represented by means of objects, and three types of membranes are considered: evolution, local store, and global store. Based on the designed membrane structure and the inherent communication mechanism, a modified differential evolution mechanism is developed to evolve the objects in the system. Under the control of the evolution–communication mechanism of the P system, the proposed fuzzy clustering algorithm achieves good fuzzy partitioning for a data set. The proposed fuzzy clustering algorithm is compared to three recently-developed and two classical clustering algorithms for five artificial and five real-life data sets. [ABSTRACT FROM AUTHOR]