Optimal Design of Mechanisms Using Simulated Annealing: Theory and Applications

1988 ◽  
Author(s):  
P. Jain ◽  
A. M. Agogino
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Computer Aided Design ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Technique ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Kinematic Synthesis ◽  
Combinatorial Optimization Problems

Abstract A new approach to optimization in kinematic synthesis of mechanisms is presented which is based on simulated annealing, a probabilistic combinatorial optimization technique. The advantage of this technique is that it converges to the global optimum asymptotically. Simulated annealing has been successfully applied to solve several combinatorial optimization problems in the fields of computer-aided design of VLSI chips, image processing and graph theory. However, it has not been used as an optimization tool in the field of mechanical design. We solve three problems in kinematic synthesis using simulated annealing and the results show that the method is fast and obtains solutions at least as good as and in some cases better than those obtained by conventional optimization techniques.

Matched Field Processing Based on the Simulated Annealing

2013 ◽  
Vol 651 ◽  
pp. 879-884
Author(s):  
Qi Wang ◽  
Ying Min Wang ◽  
Yan Ni Gou
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Mediterranean Sea ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Combinatorial Optimization Problem ◽  
Matched Field Processing ◽  
Combinatorial Optimization Problems ◽  
Passive Localization

The matched field processing (MFP) for localization usually needs to match all the replica fields in the observation sea with the received fields, and then find the maximum peaks in the matched results, so how to find the maximum in the results effectively and quickly is a problem. As known the classical simulated annealing (CSA) which has the global optimization capability is used widely for combinatorial optimization problems. For passive localization the position of the source can be recognized as a combinatorial optimization problem about range and depth, so a new matched field processing based on CSA is proposed. In order to evaluate the performance of this method, the normal mode was used to calculate the replica field. Finally the algorithm was evaluated by the dataset in the Mediterranean Sea in 1994. Comparing to the conventional matched field passive localization (CMFP), it can be conclude that the new one can localize optimum peak successfully where the output power of CMFP is maximum, meanwhile it is faster than CMFP.

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.

scholarly journals Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaodong Yan ◽  
Jiahui Ma ◽  
Tong Wu ◽  
Aoyang Zhang ◽  
Jiangbin Wu ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Tin Oxide ◽  
Optimization Problems ◽  
Process Efficiency ◽  
Np Hard ◽  
Boltzmann Machine ◽  
Combinatorial Optimization Problems ◽  
Cooling Strategies ◽  
Neuromorphic Hardware

AbstractNeuromorphic hardware implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a reconfigurable tin oxide (SnOx)/molybdenum disulfide (MoS2) heterogeneous memristive device that can realize tunable stochastic dynamics in its output sampling characteristics. The device can sample exponential-class sigmoidal distributions analogous to the Fermi-Dirac distribution of physical systems with quantitatively defined tunable “temperature” effect. A BM composed of these tunable stochastic neuron devices, which can enable simulated annealing with designed “cooling” strategies, is conducted to solve the MAX-SAT, a representative in NP-hard combinatorial optimization problems. Quantitative insights into the effect of different “cooling” strategies on improving the BM optimization process efficiency are also provided.

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 Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 181 ◽  
Author(s):  
Freddy Lucay ◽  
Edelmira Gálvez ◽  
Luis Cisternas
Keyword(s):  
Tabu Search ◽  
Approximate Method ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Adaptive Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

Simulated annealing methods with general acceptance probabilities

1987 ◽  
Vol 24 (03) ◽  
pp. 657-667 ◽  
Author(s):  
S. Anily ◽  
A. Federgruen
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Local Optimum ◽  
Heuristic Solution ◽  
General Acceptance ◽  
Combinatorial Optimization Problems ◽  
Local Neighborhood ◽  
Starting Solution ◽  
Solution Methods

Heuristic solution methods for combinatorial optimization problems are often based on local neighborhood searches. These tend to get trapped in a local optimum and the final result is often heavily dependent on the starting solution. Simulated annealing methods attempt to avoid these problems by randomizing the procedure so as to allow for occasional changes that worsen the solution. In this paper we provide probabilistic analyses of different designs of these methods.

Sign in / Sign up

Export Citation Format

Share Document