Synchronizability Optimization for Tree-structured Deterministic Small-world Network with Modified Simulated Annealing Algorithm

2012 ◽  
Author(s):  
Kang Guang-Yu ◽  
Xin-Feng Li ◽  
Zhe-Ming Lu ◽  
Yu-Xin Su
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Small World ◽  
Small World Network ◽  
Annealing Algorithm

scholarly journals A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haifeng Du ◽  
Jiarui Fan ◽  
Xiaochen He ◽  
Marcus W. Feldman
Keyword(s):  
Simulated Annealing ◽  
Network Structure ◽  
Simulated Annealing Algorithm ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Network ◽  
Small World Networks ◽  
Experimental Systems ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Network structure is an important component of analysis in many parts of the natural and social sciences. Optimization of network structure in order to achieve specific goals has been a major research focus. The small-world network is known to have a high average clustering coefficient and a low average path length. Previous studies have introduced a series of models to generate small-world networks, but few focus on how to improve the efficiency of the generating process. In this paper, we propose a genetic simulated annealing (GSA) algorithm to improve the efficiency of transforming other kinds of networks into small-world networks by adding edges, and we apply this algorithm to some experimental systems. In the process of using the GSA algorithm, the existence of hubs and disassortative structure is revealed.

A New Solution for Maxterm Problem in Trigonometric Functions by Simulated Annealing Algorithm

2013 ◽  
Vol 4 (2) ◽  
pp. 20-28
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Hadi Najafi ◽  
Kourosh Farahkhah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Trigonometric Functions ◽  
Matlab Software ◽  
Annealing Algorithm ◽  
Do So

The present paper is an attempt to get total minimum of trigonometric Functions by Simulated Annealing. To do so the researchers ran Simulated Annealing. Sample trigonometric functions and showed the results through Matlab software. According the Simulated Annealing Solves the problem of getting stuck in a local Maxterm and one can always get the best result through the Algorithm.

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