Membrane Computing Model Design with Quantum-Inspired Evolutionary Algorithms

2013 ◽  
Vol 655-657 ◽  
pp. 1761-1764 ◽  
Author(s):  
Hai Na Rong ◽  
Xiao Li Huang
Keyword(s):  
Membrane Computing ◽  
Design Method ◽  
Main Idea ◽  
Natural Computing ◽  
Automatic Design ◽  
Model Design ◽  
Evolution Rule ◽  
Computing Model ◽  
Rule Set ◽  
Computing Models

As a branch of natural computing, membrane computing has attracted much attention in various disciplines. But the programmability of membrane computing models is an ongoing and challenging issue in this area. This paper develops the automatic design of membrane computing models through predefining the membrane structure and initial objects and introducing a modified quantum-inspired evolutionary algorithm with a local disturbance to select an appropriate subset from a redundant evolution rule set. The main idea of the presented method is that multiple membrane computing models, instead of only one model like in the literature, can be designed by applying one redundant evolution rule set. The effectiveness of the design method is verified by the experiments.

Software for Membrane Computing

2020 ◽  
pp. 659-678
Author(s):  
Andrei George Florea ◽  
Cătălin Buiu
Keyword(s):  
Real World ◽  
Membrane Computing ◽  
Real Life ◽  
Natural Computing ◽  
Software Application ◽  
The World ◽  
Definition Of ◽  
And Robotics ◽  
Computing Models ◽  
Detailed Presentation

In order to use membrane computing models for real life applications there is a real need for software that can read a model from some form of input media and afterwards execute it according to the execution rules that are specified in the definition of the model. Another requirement of this software application is for it to be capable of interfacing the computing model with the real world. This chapter discusses how this problem was solved along the years by various researchers around the world. After presenting notable examples from the literature, the discussion continues with a detailed presentation of three membrane computing simulators that have been developed by the authors at the Laboratory of Natural Computing and Robotics at the Politehnica University of Bucharest, Romania.

The P versus NP Problem from the Membrane Computing View

2014 ◽  
Vol 22 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Mario J. Pérez-Jiménez
Keyword(s):  
Membrane Computing ◽  
Efficient Solutions ◽  
Significant Advance ◽  
Natural Computing ◽  
Complexity Classes ◽  
Membrane Systems ◽  
Hard Problems ◽  
Young Branch ◽  
Np Problem ◽  
Computing Models

In the last few decades several computing models using powerful tools from Nature have been developed (because of this, they are known as bio-inspired models). Commonly, the space-time trade-off method is used to develop efficient solutions to computationally hard problems. According to this, implementation of such models (in biological, electronic, or any other substrate) would provide a significant advance in the practical resolution of hard problems. Membrane Computing is a young branch of Natural Computing initiated by Gh. Păun at the end of 1998. It is inspired by the structure and functioning of living cells, as well as from the organization of cells in tissues, organs, and other higher order structures. The devices of this paradigm, called P systems or membrane systems, constitute models for distributed, parallel and non-deterministic computing. In this paper, a computational complexity theory within the framework of Membrane Computing is introduced. Polynomial complexity classes associated with different models of cell-like and tissue-like membrane systems are defined and the most relevant results obtained so far are presented. Different borderlines between efficiency and non-efficiency are shown, and many attractive characterizations of the P ≠ NP conjecture within the framework of this bio-inspired and non-conventional computing model are studied.

Software for Membrane Computing

2017 ◽  
pp. 35-58
Keyword(s):  
Real World ◽  
Membrane Computing ◽  
Real Life ◽  
Natural Computing ◽  
Software Application ◽  
The World ◽  
Definition Of ◽  
And Robotics ◽  
Computing Models ◽  
Detailed Presentation

In order to use membrane computing models for real life applications there is a real need for software that can read a model from some form of input media and afterwards execute it according to the execution rules that are specified in the definition of the model. Another requirement of this software application is for it to be capable of interfacing the computing model with the real world. This chapter discusses how this problem was solved along the years by various researchers around the world. After presenting notable examples from the literature, the discussion continues with a detailed presentation of three membrane computing simulators that have been developed by the authors at the Laboratory of Natural Computing and Robotics at the Politehnica University of Bucharest, Romania.

scholarly journals From NP-Completeness to DP-Completeness: A Membrane Computing Perspective

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Luis Valencia-Cabrera ◽  
David Orellana-Martín ◽  
Miguel Á. Martínez-del-Amor ◽  
Ignacio Pérez-Hurtado ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Lower Bound ◽  
Polynomial Time ◽  
Time Complexity ◽  
Membrane Computing ◽  
Decision Problems ◽  
Membrane Systems ◽  
Np Completeness ◽  
Computing Model ◽  
Complete Problems ◽  
Computing Models

Presumably efficient computing models are characterized by their capability to provide polynomial-time solutions for NP-complete problems. Given a class ℛ of recognizer membrane systems, ℛ denotes the set of decision problems solvable by families from ℛ in polynomial time and in a uniform way. PMCℛ is closed under complement and under polynomial-time reduction. Therefore, if ℛ is a presumably efficient computing model of recognizer membrane systems, then NP ∪ co-NP ⊆ PMCℛ. In this paper, the lower bound NP ∪ co-NP for the time complexity class PMCℛ is improved for any presumably efficient computing model ℛ of recognizer membrane systems verifying some simple requirements. Specifically, it is shown that DP ∪ co-DP is a lower bound for such PMCℛ, where DP is the class of differences of any two languages in NP. Since NP ∪ co-NP ⊆ DP ∩ co-DP, this lower bound for PMCℛ delimits a thinner frontier than that with NP ∪ co-NP.

IoT based on Membrane Computing Models

2021 ◽  
Author(s):  
Ababii Victor ◽  
Sudacevschi Viorica ◽  
Munteanu Silvia ◽  
Borozan Olesea ◽  
Nistiriuc Ana ◽  
...  
Keyword(s):  
Membrane Computing ◽  
Computing Models

Multi-Model Adaptive Regulation for Aircraft Dynamics With Different Zero Structures

2014 ◽  
Author(s):  
Eric D. Peterson ◽  
Harry G. Kwatny
Keyword(s):  
State Feedback ◽  
Design Method ◽  
Linear Matrix ◽  
Model Design ◽  
Multiple Model ◽  
Matrix Inequalities ◽  
Adaptive Regulation ◽  
Aircraft Dynamics ◽  
Adaptive Regulator ◽  
Parameter Dependent

An adaptive regulator is designed for parameter dependent families of systems subject to changes in the zero structure. Since continuous adaptive regulation is limited by relative degree and right half plane zeros, a multiple model adaptive regulator is implemented. The two multiple model design subproblems, covering and switching, are addressed with LQR state feedback and Lyapunov function switch logic respectively. These two subproblems are combined into a set of Linear Matrix Inequalities (LMI) and concurrently solved. The multiple model design method is applied to longitudinal aircraft dynamics.

A Complementary Approach to Grid and Cloud Distributed Computing Paradigms

2012 ◽  
pp. 1929-1942
Author(s):  
Mehdi Sheikhalishahi ◽  
Manoj Devare ◽  
Lucio Grandinetti ◽  
Maria Carmen Incutti
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Grid Architecture ◽  
Computing Model ◽  
Complementary Approach ◽  
The Future ◽  
Original Grid ◽  
Computing Models

Cloud computing is a new kind of computing model and technology introduced by industry leaders in recent years. Nowadays, it is the center of attention because of various excellent promises. However, it brings some challenges and arguments among computing leaders about the future of computing models and infrastructure. For example, whether it is going to be in place of other technologies in computing like grid or not, is an interesting question. In this chapter, we address this issue by considering the original grid architecture. We show how cloud can be put in the grid architecture to complement it. As a result, we face some shadow challenges to be addressed.

Comparator Synthesis Using Distributed Genetic Algorithm and HSPICE Optimization

2019 ◽  
Vol 888 ◽  
pp. 17-28
Author(s):  
Nobukazu Takai ◽  
Kento Suzuki ◽  
Yoshiki Sugawara
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Design Method ◽  
Automatic Design ◽  
Circuit Topology ◽  
Target Value ◽  
Distributed Genetic Algorithm ◽  
Better Than

In this paper, we propose an automatic design method that determines comparator topology and satisfies desired specification of the comparator by combining distributed genetic algorithm and HSPICE optimization function.In the comparator synthesis, new topology is created using known circuit topology information.After creating the topology, optimization of element values of the comparator is executed by distributed genetic algorithm and HSPICE optimization.As a target value example, specification of HA163S02 is used.Simulation results indicate that the proposed method can design the comparator despite the number of specifications and elements of circuit increase compared to the conventional methods.Furthermore, the performance of the automatic designed comparator is better than that of conventional comparators.

Membrane Computing Model for Software Requirement Engineering

2010 ◽  
Author(s):  
Velayutham Pavanasam ◽  
Chandrasekaran Subramaniam ◽  
Thulukkanam Srinivasan ◽  
Jitendra Kumar Jain D
Keyword(s):  
Membrane Computing ◽  
Requirement Engineering ◽  
Computing Model ◽  
Software Requirement
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