Solving the 0-1 knapsack problem based on a parallel intelligent molecular computing model system

2017 ◽  
Vol 33 (5) ◽  
pp. 2719-2726
Author(s):  
Zuwen Ji ◽  
Zhaocai Wang ◽  
Tunhua Wu ◽  
Wei Huang
Keyword(s):  
Knapsack Problem ◽  
Model System ◽  
Molecular Computing ◽  
Computing Model

A Molecular Computing Model for Maximum Independent Set Based on Origami and Greedy Algorithm

2014 ◽  
Vol 11 (8) ◽  
pp. 1773-1778 ◽  
Author(s):  
Wang Xixu ◽  
Li Jing ◽  
Song Zhichao ◽  
Jing Yang ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Greedy Algorithm ◽  
Independent Set ◽  
Molecular Computing ◽  
Maximum Independent Set ◽  
Computing Model

A “Nano-Dial” Molecular Computing Model Based on Circular DNA

2010 ◽  
Vol 6 (3) ◽  
pp. 285-291
Author(s):  
Cheng Zhang ◽  
Jing Yang ◽  
Jin Xu ◽  
Shudong Wang
Keyword(s):  
Molecular Computing ◽  
Circular Dna ◽  
Computing Model ◽  
Model Based

Research on Construction of E-Intelligence Maintenance System

2011 ◽  
Vol 421 ◽  
pp. 536-539
Author(s):  
Meng Ge ◽  
Yan Li Jiao
Keyword(s):  
Manufacturing Industry ◽  
Model System ◽  
Global Market ◽  
Rapid Expansion ◽  
Maintenance System ◽  
Computing Model ◽  
Maintenance Service ◽  
Set Up ◽  
Net Framework ◽  
Grid Based

Taking on the features of agility, intelligence, Internet and socialization in the wake of rapid expansion of global manufacture and cutthroat competition, to establish an e-intelligence maintenance system is crucial for manufacturing enterprise to reduce costs and increase profits in the global market. Based on the analysis of requirement for e-intelligence maintenance in manufacturing industry of our country, this paper puts forward a total solution to e- intelligence maintenance system. Moreover, it expounds in particular the system realizing technology, viz R&D methods and process to set up a framework based on .NET Framework. The key realizing technology is including grid-based computing model, system prototyping development, and development of personalized maintenance service kit.

A Molecular Computing Model for 0-1 Programming Problem Using DNA Nanoparticles

2013 ◽  
Vol 10 (10) ◽  
pp. 2380-2384
Author(s):  
Jing Yang ◽  
Cheng Zhang ◽  
Shi Liu ◽  
Hong Xia ◽  
Jin Xu
Keyword(s):  
Programming Problem ◽  
Molecular Computing ◽  
Dna Nanoparticles ◽  
Computing Model

A Molecular Computing Model for Graph Coloring Problem Using DNA Quantum Dot

2015 ◽  
Vol 12 (7) ◽  
pp. 1272-1276
Author(s):  
Jiawei Li ◽  
Zhichao Song ◽  
Cheng Zhang ◽  
Jing Yang ◽  
H. Inaki Schlaberg ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Graph Coloring ◽  
Molecular Computing ◽  
Coloring Problem ◽  
Computing Model ◽  
Graph Coloring Problem

scholarly journals An Adaptive Optimization Spiking Neural P System for Binary Problems

2020 ◽  
Vol 31 (01) ◽  
pp. 2050054 ◽  
Author(s):  
Ming Zhu ◽  
Qiang Yang ◽  
Jianping Dong ◽  
Gexiang Zhang ◽  
Xiantai Gou ◽  
...  
Keyword(s):  
Knapsack Problem ◽  
Membrane Computing ◽  
Combinatorial Problems ◽  
P Systems ◽  
P System ◽  
Adaptive Optimization ◽  
Spiking Neural P System ◽  
Multiple Faults ◽  
Computing Model ◽  
Bus System

Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system.

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