A Wavelet Chaotic Simulated Annealing Neural Network and Its Application to Optimization Problems

2011 ◽  
Author(s):  
Yunxiao Jiang ◽  
Yingke Lei ◽  
Zifa Zhong ◽  
Xiang Zou
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Chaotic Simulated Annealing

SIMULATED ANNEALING SCHEMES IN TRANSIENTLY CHAOTIC NEURAL NETWORK MODEL

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2579-2584 ◽  
Author(s):  
Y. C. FENG ◽  
X. CAI
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Neural Network Model ◽  
Approximation Method ◽  
Optimization Problems ◽  
Chaotic Neural Network ◽  
Combinatorial Optimization Problems ◽  
Search Speed ◽  
Evolution Function

A transiently chaotic neural network (TCNN) is an approximation method for combinatorial optimization problems. The evolution function of self-back connect weight, called annealing function, influences the accurate and search speed of TCNN model. This paper analyzes two common annealing schemes. Furthermore we proposed a new subsection exponential annealing function. Finally, we compared these annealing schemes in TSP problem.

Chaotic Walk in Simulated Annealing Search Space for Task Allocation in a Multiprocessing System

2013 ◽  
Vol 7 (3) ◽  
pp. 58-79 ◽  
Author(s):  
Ken Ferens ◽  
Darcy Cook ◽  
Witold Kinsner
Keyword(s):  
Simulated Annealing ◽  
Task Allocation ◽  
Intelligent Systems ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Global Optimum ◽  
Processor Allocation ◽  
Combinatorial Optimization Problems ◽  
Chaotic Simulated Annealing

This paper proposes the application of chaos in large search space problems, and suggests that this represents the next evolutionary step in the development of adaptive and intelligent systems towards cognitive machines and systems. Three different versions of chaotic simulated annealing (XSA) were applied to combinatorial optimization problems in multiprocessor task allocation. Chaotic walks in the solution space were taken to search for the global optimum or “good enough” task-to-processor allocation solutions. Chaotic variables were generated to set the number of perturbations made in each iteration of a XSA algorithm. In addition, parameters of a chaotic variable generator were adjusted to create different chaotic distributions with which to search the solution space. The results show that the convergence rate of the XSA algorithm is faster than simulated annealing when the solutions are far apart in the solution space. In particular, the XSA algorithms found simulated annealing’s best result on average about 4 times faster than simulated annealing.

scholarly journals Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems

2020 ◽  
Vol 6 (33) ◽  
pp. eaba9901
Author(s):  
Ke Yang ◽  
Qingxi Duan ◽  
Yanghao Wang ◽  
Teng Zhang ◽  
Yuchao Yang ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Hopfield Network ◽  
Local Optima ◽  
Daily Lives ◽  
Intrinsic Nonlinearity ◽  
Max Cut Problem ◽  
Chaotic Simulated Annealing ◽  
Memristor Crossbar ◽  
Fully Connected

Optimization problems are ubiquitous in scientific research, engineering, and daily lives. However, solving a complex optimization problem often requires excessive computing resource and time and faces challenges in easily getting trapped into local optima. Here, we propose a memristive optimizer hardware based on a Hopfield network, which introduces transient chaos to simulated annealing in aid of jumping out of the local optima while ensuring convergence. A single memristor crossbar is used to store the weight parameters of a fully connected Hopfield network and adjust the network dynamics in situ. Furthermore, we harness the intrinsic nonlinearity of memristors within the crossbar to implement an efficient and simplified annealing process for the optimization. Solutions of continuous function optimizations on sphere function and Matyas function as well as combinatorial optimization on Max-cut problem are experimentally demonstrated, indicating great potential of the transiently chaotic memristive network in solving optimization problems in general.

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