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.

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.

Improved Hybrid Taboo Search with its Application in Computer-Aided Optimization Problem

2012 ◽  
Vol 488-489 ◽  
pp. 1293-1297
Author(s):  
Jia Yang Wang ◽  
Bi Zhang ◽  
Zuo Yong Li ◽  
Lei Xu
Keyword(s):  
Simulated Annealing ◽  
Computer Aided Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Test Functions ◽  
Local Optima ◽  
Taboo Search ◽  
Computer Aided ◽  
Aided Design ◽  
Improved Algorithm

A new improved algorithm of Taboo Search (TS), namely, Hybrid Taboo Search (HTS) is first introduced and tried for several test functions having multiple local optima. Here, Taboo Search was improved by combining Immune Arithmetic (IA) and Simulated Annealing (SA). Several strategies to improve the TS have been presented before, but the focus here is on the novelty, availability and precision of algorithm. There are several optimization problems in computer-aided design, so the article used the improved HTS in computer-aided optimization problems, the performance of which is compared with the performance of conventional TS (TS). Results show that HTS plays an important role in solving computer-aided optimization problems with the effectiveness and higher accuracy.

The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems

2003 ◽  
Vol 125 (1) ◽  
pp. 141-146 ◽  
Author(s):  
A. J. Knoek van Soest ◽  
L. J. R. Richard Casius
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Simplex Algorithm ◽  
Programming Algorithm ◽  
Parallel Genetic Algorithm ◽  
Local Optima ◽  
Optimal Region ◽  
Downhill Simplex

A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical and biomechanical optimization problems is compared to a sequential quadratic programming algorithm, a downhill simplex algorithm and a simulated annealing algorithm. When high-dimensional non-smooth or discontinuous problems with numerous local optima are considered, only the simulated annealing and the genetic algorithm, which are both characterized by a weak search heuristic, are successful in finding the optimal region in parameter space. The key advantage of the genetic algorithm is that it can easily be parallelized at negligible overhead.

scholarly journals Simulated Annealing with Gaussian Probability Density Function for Transmission Expansion Planning

2017 ◽  
Vol 1 (1) ◽  
pp. 104-113
Author(s):  
Phillipe Gomes
Keyword(s):  
Simulated Annealing ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Optimization Problems ◽  
Local Optima ◽  
Gaussian Probability Density Function ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion

Simulated Annealing (SA) is a powerful tool for optimization problems that have several local optima. This tool has the ability to escape from a local optima accepting relatively bad solutions for a period and searching for good solutions in your neighborhood. This paper describes the use of SA based on Gaussian Probability Density Function as a decision support criteria in resolution of Transmission Expansion Planning (TEP) problem. This method consists in starting from an initial solution with all possible circuits added and over the iterations removing, replacing or adding new circuits. The method proved to be a reasonable computational effort and proved able to find optimal values known in the literature.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

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