Chemical relevance of a pure combinatorial problem: Isomers of conjugated polyenes

1996 ◽  
Vol 7 (3) ◽  
pp. 183-186 ◽  
Author(s):  
S. J. Cyvin ◽  
E. K. Lloyd ◽  
B. N. Cyvin ◽  
J. Brunvoll
Keyword(s):  
Combinatorial Problem ◽  
Conjugated Polyenes

A Combinatorial Problem Which Is Complete in Polynomial Space

1976 ◽  
Vol 23 (4) ◽  
pp. 710-719 ◽  
Author(s):  
S. Even ◽  
R. E. Tarjan
Keyword(s):  
Combinatorial Problem ◽  
Polynomial Space

scholarly journals Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kuan-Cheng Lin ◽  
Sih-Yang Chen ◽  
Jason C. Hung
Keyword(s):  
Feature Selection ◽  
Parameter Optimization ◽  
Combinatorial Problem ◽  
Combinatorial Problems ◽  
Support Vector ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Information And Communication

Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA) employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

Metallocenophanes, VII. Preparation and Characterisation of the Disilver(I) Complexes of Conjugated Polyenes with [2.2.2]Paracyclophanyl End Groups

1995 ◽  
Vol 128 (11) ◽  
pp. 1079-1082 ◽  
Author(s):  
Fenton R. Heirtzler ◽  
Henning Hopf ◽  
Peter Bubenitschek ◽  
Peter G. Jones
Keyword(s):  
End Groups ◽  
Conjugated Polyenes

Conformational analysis of conjugated polyenes by nuclear magnetic resonance and low resolution microwave spectroscopy

1979 ◽  
Vol 83 (11) ◽  
pp. 1540-1545 ◽  
Author(s):  
Wayne E. Steinmetz ◽  
James E. Pollard ◽  
Jeffrey M. Blaney ◽  
Bruce K. Winker ◽  
In Ki. Mun ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Conformational Analysis ◽  
Microwave Spectroscopy ◽  
Low Resolution ◽  
Conjugated Polyenes ◽  
Nuclear Magnetic

scholarly journals Actor-critic learning-based energy optimization for UAV access and backhaul networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yaxiong Yuan ◽  
Lei Lei ◽  
Thang X. Vu ◽  
Symeon Chatzinotas ◽  
Sumei Sun ◽  
...  
Keyword(s):  
Power Allocation ◽  
Heuristic Algorithms ◽  
Piecewise Linear ◽  
Access Network ◽  
Computation Time ◽  
Combinatorial Problem ◽  
Base Station ◽  
Piecewise Linear Approximation ◽  
Group Scheduling ◽  
User Group

AbstractIn unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

A parallel biological computing algorithm to solve the vertex coloring problem with polynomial time complexity

2021 ◽  
pp. 1-11
Author(s):  
Zhaocai Wang ◽  
Dangwei Wang ◽  
Xiaoguang Bao ◽  
Tunhua Wu
Keyword(s):  
Time Complexity ◽  
Optimization Problems ◽  
Combinatorial Problem ◽  
Vertex Coloring ◽  
Intelligent Computing ◽  
Coloring Problem ◽  
Combinatorial Optimization Problems ◽  
Computing Algorithm ◽  
Vertex Coloring Problem ◽  
Dna Algorithm

The vertex coloring problem is a well-known combinatorial problem that requires each vertex to be assigned a corresponding color so that the colors on adjacent vertices are different, and the total number of colors used is minimized. It is a famous NP-hard problem in graph theory. As of now, there is no effective algorithm to solve it. As a kind of intelligent computing algorithm, DNA computing has the advantages of high parallelism and high storage density, so it is widely used in solving classical combinatorial optimization problems. In this paper, we propose a new DNA algorithm that uses DNA molecular operations to solve the vertex coloring problem. For a simple n-vertex graph and k different kinds of color, we appropriately use DNA strands to indicate edges and vertices. Through basic biochemical reaction operations, the solution to the problem is obtained in the O (kn2) time complexity. Our proposed DNA algorithm is a new attempt and application for solving Nondeterministic Polynomial (NP) problem, and it provides clear evidence for the ability of DNA calculations to perform such difficult computational problems in the future.

Optimization of Restoration Paths of Power System after Blackout

2013 ◽  
Vol 347-350 ◽  
pp. 1467-1472
Author(s):  
Wen Wei Huang ◽  
Gang Yao ◽  
Xiao Yan Qiu ◽  
Nian Liu ◽  
Guang Tang Chen
Keyword(s):  
Power System ◽  
Transmission Lines ◽  
Reactive Power ◽  
Minimum Spanning Tree ◽  
Operation Time ◽  
Combinatorial Problem ◽  
Weather Conditions ◽  
Recovery Process ◽  
Point Of View ◽  
Improved Genetic Algorithm

Optimization of restoration paths of power system after blackout is a multi-stage, multi-target, multi-variable combinatorial problem in the power system restoration. This paper presents a reasonable model and effectually method. The proposed model is considered as a typical partial minimum spanning tree problem from the mathematical point of view which considering all kinds of constraints. Improved data envelopment analysis (DEA) was used to get the weight which considering line charging reactive power, weather conditions, operation time and betweenness of transmission lines. The improved genetic algorithm method is employed to solve this problem. Finally, an example is given which proves the strategy of the line restoration can effectively handle the uncertainty of the system recovery process, to guarantee the system successfully restored after the catastrophic accidents.

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