Analyzing radar emitter signals with membrane algorithms

Zhang, Gexiang; Liu, Chunxiu; Rong, Haina

doi:10.1016/j.mcm.2010.06.002

Cited by 39 publications

(19 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the polarization of membrane i′ is translated from + to  , and t 4 becomes t 5 . In the next step, in membrane i′, only the rules of types (17) and (19) can be used, and in membrane i, the rule of type (11) can be used. Therefore, the second worst DNA sequence is deleted in membrane i, and the polarization of membrane i′ becomes 0 again and t 5 becomes t 0 again.…”

Section: Membrane Evolutionary Algorithm For Dna Sequence Designmentioning

confidence: 99%

“…A membrane algorithm was also employed to solve the minimum storage problem [15]. The quantum-inspired evolutionary algorithm based on P systems was also developed to solve the knapsack problem and the radar emitter signals problem [16,17]. The similarities between distributed evolutionary algorithms and P systems have been analyzed and new variants of distributed evolutionary algorithms were suggested and applied for some continuous optimization problems [18].…”

mentioning

confidence: 99%

See 1 more Smart Citation

A membrane evolutionary algorithm for DNA sequence design in DNA computing

Xiao

Zhang

2012

Chin. Sci. Bull.

View full text Add to dashboard Cite

DNA sequence design has a crucial role in successful DNA computation, which has been proved to be an NP-hard (non-deterministic polynomial-time hard) problem. In this paper, a membrane evolutionary algorithm is proposed for the DNA sequence design problem. The results of computer experiments are reported, in which the new algorithm is validated and out-performs certain known evolutionary algorithms for the DNA sequence design problem.DNA computing, membrane computing, P system, DNA sequence design Citation:Xiao J H, Zhang X Y, Xu J. A membrane evolutionary algorithm for DNA sequence design in DNA computing. Chin Sci Bull, 2012Bull, , 57: 698706, doi: 10.1007 Membrane computing is an emergent branch of natural computing, first introduced by Paun [1]. This unconventional model of computation is a type of distributed parallel system, which is inspired by the structure and function of living cells. The devices of this model are called P systems.Roughly speaking, a P system consists of a cell-like membrane structure, in the compartments of which there are multisets of objects that evolve according to given rules in a synchronous, non-deterministic maximally parallel manner. Many different classes of such computing devices have already been investigated. Most of them are computationally universal, i.e., able to compute whatever a Turing machine can do [2][3][4], and are computationally efficient, i.e., able to trade space for time and in this way solve presumably intractable problems in a feasible time (e.g., [5][6][7][8][9]). Membrane computing is very attractive from a computational point of view because of its hierarchical structure and intrinsic parallelism. Evolutionary algorithms are robust optimization and search methods inspired by evolutionary processes occurring in natural selection and molecular genetics. They are approximation algorithms that seek a trade-off between solution quality and computational costs. The main features of evolutionary algorithms are the representation and evaluation of individuals, population dynamics, and evolutionary operators, such as selection, crossover, and mutation [10]. Evolutionary algorithms exhibit an intrinsic parallelism derived from dealing with multiple individuals, show remarkable adaptability and flexibility to various applications, and provide good search capabilities and robust results [11].Both P systems and evolutionary algorithms are natureinspired models and are used to solve complex problems; however, they are different with respect to the objects and rules used and in the computational strategies employed. While P systems represent a suitable formal framework for parallel-distributed computation, evolutionary algorithms are very effective for implementing different algorithms to solve numerous problems [11]. Thus, the possible interaction between P systems and evolutionary algorithms represents a fertile research field, as suggested by the list of 26 open problems in membrane computing [12]. The first attempt in this direction was by Nishida [13,14], who ...

show abstract

Section: Membrane Evolutionary Algorithm For Dna Sequence Designmentioning

confidence: 99%

mentioning

confidence: 99%

A membrane evolutionary algorithm for DNA sequence design in DNA computing

Xiao

Zhang

2012

Chin. Sci. Bull.

View full text Add to dashboard Cite

show abstract

“…Aiming at investigating the interactions between membrane computing and evolutionary computation, membrane-inspired evolutionary algorithms (MIEAs) are considered as a class of hybrid optimization algorithms, which use the concepts and principles of meta-heuristic search methodologies and the hierarchical or network structures of P systems, and to some extent, some of the rules of P systems [7,8]. A MIEA is regarded as a successful paradigm extending P system models with capabilities that make them amenable to real-world applications [9].…”

Section: Introductionmentioning

confidence: 99%

“…A well-known NP-complete optimization problem, knapsack problem, was used to carry out extensive experiments, which show that QEPS achieves better solutions than its counterpart QIEA and OLMS has an advantage over NMS. In [7,[13][14][15], QEPS and its modified versions were presented to solve various problems, such as radar emitter signal analysis and image processing. In [16] and [17], DNA sequences design was optimized by designing a MIEA based on crossover and mutation rules and a dynamic MIEA combining the fusion and division rules of P systems with active membranes and search strategies of differential evolution (DE) and particle swarm optimization (PSO), respectively.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms

Zhang

Cheng²,

Gheorghe

2014

INT J COMPUT COMMUN

View full text Add to dashboard Cite

A membrane-inspired evolutionary algorithm (MIEA) is a successful instance of a model linking membrane computing and evolutionary algorithms. This paper proposes the analysis of dynamic behaviors of MIEAs by introducing a set of population diversity and convergence measures. This is the first attempt to obtain additional insights into the search capabilities of MIEAs. The analysis is performed on the MIEA, QEPS (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, QIEA (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better understand their characteristics and performances. Also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of MIEAs. Results show that QEPS can achieve better balance between convergence and diversity than QIEA, which indicates QEPS has a stronger capacity of balancing exploration and exploitation than QIEA in order to prevent premature convergence that might occur. Experiments utilizing knapsack problems support the above made statement.

show abstract

“…proposed a hybrid QSEA based on chaotic search to solve the DNA sequences design problem. Zhang, Liu, and Rong (2010) developed a quantum-inspired evolutionary algorithm based on P systems to analyse the radar emitter signals in modern electronic reconnaissance systems. Mariani et al (2012) proposed a new quantum particle swarm optimisation approach combined with Zaslavskii chaotic map sequences to optimise heat exchangers problem.…”

Section: Introductionmentioning

confidence: 99%

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

Xiao

Liu

Huang

et al. 2013

International Journal of Production Research

View full text Add to dashboard Cite

The selection of cooperative partners is one of the most important factors when creating a virtual enterprise, and has been extensively researched in recent years. It is difficult to solve the selection problem using the traditional optimisation methods because the simultaneous consideration of many aspects of the problem is required, such as the running cost, the reaction time and the running risk. In this paper, an adaptive quantum swarm evolutionary algorithm with time-varying acceleration coefficients is proposed for the partner selection optimisation problem. Simulation and experimental results demonstrate that the improved algorithm is valid and outperforms other evolutionary algorithms.

show abstract

Analyzing radar emitter signals with membrane algorithms

Cited by 39 publications

References 19 publications

A membrane evolutionary algorithm for DNA sequence design in DNA computing

A membrane evolutionary algorithm for DNA sequence design in DNA computing

Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms

An adaptive quantum swarm evolutionary algorithm for partner selection in virtual enterprise

Contact Info

Product

Resources

About