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Function Optimization Using IBM Q
Published in Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves, Hybrid Quantum Metaheuristics, 2022
Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves
Simulated Annealing (SA) uses a thermodynamic evolution process to search minimum energy states [3]. The primary objective of simulated annealing is to find the global minimum of a function that characterizes large and complex systems. It provides a powerful tool for solving non-convex optimization problems. Simulated annealing starts from a random initial solution. It then proceeds by generating new solution and accepting/rejecting them probabilistically. As the search proceeds, the temperature cools down and the process converges to a global minima. The temperature needs to be reduced at a slow and controlled rate to ensure proper solidification with a low energy crystalline state that corresponds to the best required result.
Simulated Annealing
Published in Anindya Ghosh, Prithwiraj Mal, Abhijit Majumdar, Advanced Optimization and Decision-Making Techniques in Textile Manufacturing, 2019
Anindya Ghosh, Prithwiraj Mal, Abhijit Majumdar
Simulated annealing was used to solve the optimization problem of Equation 11.26 in order to obtain the predefined yarn strength by searching the optimum values of cotton fiber parameters such as linear density Ttf, breaking strain af, breaking stress Q(ε), and mean length lf, as well as yarn twist/meter t. The values of initial temperature T, minimum temperature Tmin, cooling rate CT, and number of iterations at each temperature n for the simulated annealing algorithm were set to 1,000; 0.0001; 0.9; and 500, respectively.
Electro-Mechanical Coupled Modeling and Optimization of Passively Damped Adaptive Composite Structures
Published in Norman M. Wereley, Inderjit Chopra, Darryll J. Pines, Twelfth International Conference on Adaptive Structures and Technologies, 2017
Robert P. Thornburgh, Aditi Chattopadhyay
Next the same plate is optimized with two 2cm×4cm AFC actuators. Each actuator is connected to a single parallel shunt circuit. In this example the objective is the minimization of the first four vibrational modes. This method is very different from the multi-mode damping circuit proposed by Wu [6] which uses four different circuits to damp the four modes. The optimized results of the circuit are shown in Fig. 5. The piezoelectric patches move to locations at 7cm×16cm and 19cm×18cm, with orientations of 15° and 45° respectively, as seen in Fig. 4. The inductor and resistor have values of 11.645 henries and 195790 ohms for the first actuator and 103.497 henries and 140420 ohms for the second actuator. It must be noted that, as with any optimization algorithm, the use of simulated annealing does not guarantee convergence to a global minimum. As seen in Fig. 5, the first three modes are damped by only two circuits, although no reduction in the fourth mode is achieved.
Phase retrieval for sparse binary signal: uniqueness and algorithm
Published in Inverse Problems in Science and Engineering, 2018
The uniqueness of binary phase retrieval is firstly studied in this paper. We show that almost all binary signals of length n can be uniquely recovered by Fourier measurements. Simulated Annealing (SA) is a probabilistic technique to approximate the global optimum of a given function. SA is often used when the searching domain is discrete. So a constrained simulated annealing algorithm is came up to deal with this binary phase retrieval problem (1.2). When it comes to the general signal, we propose a two-stage method to recover the signal. Firstly, we use the constrained SA to get the signal’s support, then we use Gespar to recover the general signal. Numerical tests show its good performance compared to the original Gespar method in the same time span.
Characterization of electromagnetic parameters through inversion using metaheuristic technique
Published in Inverse Problems in Science and Engineering, 2021
Mohamed Elkattan, Aladin Kamel
In our inversion setting, we present a strategy where the forward solver (or at least part of it) will be used repeatedly for different values of the unknown conductivity and permittivity in a form of a global optimization problem that we then solve using a simulated annealing algorithm. Simulated annealing is a probabilistic technique for approximating the global optimum of a given function [18]. Specifically, it gives an approximate global optimization in a large search space. For problems where finding the precise global optimum is less important than finding an acceptable global optimum in a fixed amount of time, simulated annealing is preferable to alternative methods that may require expensive computations and need much time to reach the final solution.
Disassembly sequence planning validated thru augmented reality for a speed reducer
Published in Cogent Engineering, 2022
Leonardo Frizziero, Giampiero Donnici, Gian Maria Santi, Christian Leon-Cardenas, Patrich Ferretti, Gaia Pascucci, Alfredo Liverani
Simulated Annealing (SA; Shuyi et al., 2014), (Dowsland & Thompson, 2012),(Hao & Hongfu, 2009), In the algorithm, the reticular defect corresponds to an incorrect combination of two objects. Simulated Annealing aims to find a global minimum when there are multiple local minima and its most common applications are combinatorial problems, in particular scheduling problems.