Solving multidimensional 0–1 knapsack problem by P systems with input and active membranes

Pan, Linqiang; Martı́n-Vide, Carlos

doi:10.1016/j.jpdc.2005.05.018

Cited by 73 publications

(25 citation statements)

References 5 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Huang Liang, et al, combining genetic algorithms and membrane computing, shrink and expand in the search area [15]. Pan Lin-Qiang use activities membrane architecture for solving 0-1 knapsack problem [16]. Nishida divided the problem space into many membranes (area) in solving the TSP problem, Tabu Search is used in some areas and Genetic algorithm is used in other areas, and compared with the simulated annealing algorithm, obtained better results [17].…”

Section: Communication P Systemsmentioning

confidence: 99%

Research of Road Pricing System Based on P System

Zhong¹,

Wei²,

Li³

2015

IJUNESST

View full text Add to dashboard Cite

show abstract

Section: Communication P Systemsmentioning

confidence: 99%

Research of Road Pricing System Based on P System

Zhong¹,

Wei²,

Li³

2015

IJUNESST

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Citationmentioning

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

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

“…P systems with active membranes can produce an exponential growth of membranes and consequently can solve a class of NP-complete problems, such as the satisfiability (SAT) problem [1,20] and the knapsack problem [18], in a linear or polynomial time. The two types of complete problems were discussed in [1,18,20] and in many other places from a mathematical perspective.…”

Section: Introductionmentioning

confidence: 99%

“…The two types of complete problems were discussed in [1,18,20] and in many other places from a mathematical perspective. To the best of our knowledge, no evolutionary algorithm using P system with active membranes has been devised to approximately solve the two aforementioned kinds of problems.…”

Section: Introductionmentioning

confidence: 99%

QEAM: An Approximate Algorithm Using P Systems with Active Membranes

Zhang

Cheng

Gheorghe

et al. 2015

INT J COMPUT COMMUN

View full text Add to dashboard Cite

This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.

show abstract

Solving multidimensional 0–1 knapsack problem by P systems with input and active membranes

Cited by 73 publications

References 5 publications

Research of Road Pricing System Based on P System

Research of Road Pricing System Based on P System

A membrane evolutionary algorithm for DNA sequence design in DNA computing

QEAM: An Approximate Algorithm Using P Systems with Active Membranes

Contact Info

Product

Resources

About