Simulated Annealing Technique Research Articles

Ground Water Level Prediction using Adaptive Neuro-Fuzzy Inference Systems and Simulated Annealing

Abstract: In Indian subcontinent the source of ground water is mainly from rainfall and partially due to the river flow, lakes, and reservoirs, which is highly nonlinear and dynamic. In this paper, the seasonal ground water levels will be predicted using the simulated Annealing (SA) based on previous seasonal rainfall and ground water levels. The study will be carried out in Lucknow district, India. The main objective of this paper is to develop a reliable groundwater level fluctuation forecasting system to generate trend forecasts. The forecasts, based on SA techniques, are then compared to actual measurements recorded during a subsequent monitoring period, for this MATLAB platform will be used for generating the SA codes.

Design of laser pulse to enhance the photo-dissociation of a tri-atomic molecular system using optimal control theory based adaptive simulated annealing technique

In this communication, we design and optimize laser pulses to enhance the dissociation of a tri-atomic molecule using optimal control theory based adaptive simulated annealing technique. ClCN is chosen as tri-atomic molecular system as molecules containing fluorine and chlorine atoms are involved in the depletion of ozone layer and also dissociative electron attachment is possible. Fourier Grid Hamiltonian based 2D mean field methodology has been used for numerical calculations. We find an effective laser pulse by combining polychromatic Gaussian pulses and polychromatic linear pulses with different frequencies and different field intensities.

Improving the efficiency of wide-angle seismic data inversion through a nonlinear algorithm: case study of the MARCONI-3 profile

Wide-angle seismic reflection/refraction (WA) surveys provide data that can be modeled to obtain lithospheric-scale P-wave velocity (VP) models. The interpretation of these datasets is often performed as a laborious and time-consuming trial-and-error procedure, in which the relevant model parameters (layer thickness and VP) are manually adjusted until the forward modeling matches the observed travel-times. In this work, we present a fully automatic iterative nonlinear approach to invert WA datasets based on the simulated annealing technique. We test our proposed approach with data from the MARCONI-3 WA profile (southern Bay of Biscay) and compare the outcome with an existing detailed interpretation, discussing the similarities between the two models and the agreement between our model and the observed travel-times.

Robust quantum control using hybrid pulse engineering

The development of efficient algorithms that generate robust quantum controls is crucial for the realization of quantum technologies. The commonly used gradient-based optimization algorithms are limited by their sensitivity to the initial guess, which affects their performance. Here we propose combining the gradient method with the simulated annealing technique to formulate a hybrid algorithm. Our numerical analysis confirms its superior convergence rate. Using the hybrid algorithm, we generate spin-selective $\ensuremath{\pi}$ pulses and employ them for experimental measurement of local noise spectra in a three-qubit nuclear magnetic resonance system. Moreover, here we describe a general method to construct noise-resilient quantum controls by incorporating noisy fields within the optimization routine of the hybrid algorithm. Upon experimental comparison with similar sequences obtained from standard algorithms, we find remarkable robustness of the hybrid sequences against dephasing errors.

Analysis of DFIG-STATCOM P2P control action using simulated annealing techniques

This paper discusses the performance of Double Fed Induction Generator (DFIG) and static synchronous compensator (STATCOM) in a transmission system using simulated annealing techniques. The rotor speed of DFIG is always changing with respect to wind speed in a nonlinear manner, which makes the system to be more unstable. Under such a condition the performance of the system is disturbed. To improve voltage stability throughout the line, an integration of STATCOM is essential at proper location of the transmission system. The STATCOM integrated DFIG system can enhance the system voltage profile and flexible flow of power in a transmission system. In case of a large disturbance or during the shunt fault condition, the performance study is very important which can be assessed using simulated annealing techniques. The proposed model present in this research work is a multi-objective optimization problem. The parameters for the objective function were identified as voltage at the point of common coupling of wind turbine and low frequency oscillation present in the post restored active power. Therefore a stochastic algorithm based on normalized simulated annealing has been applied where the performance of the system can be tested. Coordinated reactive power control combining with DFIG and STATCOM has been analysed together during various shunt fault condition. To achieve better performance of the system Low voltage rides through the capability of the wind farm and FRT (Fault Ride Through) have been tested under the presence of STATCOM including mutual coordinated control action.

Optimal Design of Automatic Generation Control Based on Simulated Annealing in Interconnected Two-Area Power System Using Hybrid PID—Fuzzy Control

Electricity demand continues to rise on a daily basis. The most difficult task is ensuring that customers have access to reliable, high-quality electricity regardless of the weather. Automatic generation control (AGC) accomplishes this by keeping the target output power and frequency constant despite load fluctuations. This paper presents a hybrid PID-fuzzy controller for optimal automatic generation control in a two-area interconnected power system. A comparative analysis of the proposed controller was performed with the PID controller. The parameters of the both controllers were developed by simulated annealing (SA) techniques in order to obtain the best dynamic performance. MATLAB Simulink software was used to simulate the models. The results of the simulation for the two-area power system based on simulated annealing using the hybrid PID–fuzzy controller showed superior performance in comparison to a conventional PID controller.

Global optimization of hyper-parameters in reservoir computing

<abstract><p>Reservoir computing has emerged as a powerful and efficient machine learning tool especially in the reconstruction of many complex systems even for chaotic systems only based on the observational data. Though fruitful advances have been extensively studied, how to capture the art of hyper-parameter settings to construct efficient RC is still a long-standing and urgent problem. In contrast to the local manner of many works which aim to optimize one hyper-parameter while keeping others constant, in this work, we propose a global optimization framework using simulated annealing technique to find the optimal architecture of the randomly generated networks for a successful RC. Based on the optimized results, we further study several important properties of some hyper-parameters. Particularly, we find that the globally optimized reservoir network has a largest singular value significantly larger than one, which is contrary to the sufficient condition reported in the literature to guarantee the echo state property. We further reveal the mechanism of this phenomenon with a simplified model and the theory of nonlinear dynamical systems.</p></abstract>

A novel modified arithmetic optimization algorithm for power system stabilizer design

The development of a novel hybrid algorithm (AOA) with the aid of simulated annealing technique is discussed in this paper. The novel algorithm, named modified arithmetic optimization algorithm (mAOA), is proposed as an effective tool for optimizing power system stabilizer (PSS) adopted in a single-machine infinite-bus power system. To perform the assessments, MATLAB/Simulink software was used. The evaluations on the proposed algorithm are initially performed using several benchmark functions that have unimodal and multimodal natures. The results are then compared with five of the other competitive approaches (arithmetic optimization algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization and gravitational search algorithm). The comparisons with respect to those algorithms demonstrate the great promise of the constructed hybrid mAOA algorithm. This shows the greater balance between global and local search stages achieved by the mAOA algorithm. The performance of the developed mAOA algorithm is also assessed through designing an optimally performing PSS for further evaluation which allows the observation of its capability for complex real world engineering problems. To do so, PSS damping controller is formulated as an optimization problem and the constructed mAOA algorithm is used to search for optimal controller parameters to demonstrate the applicability and the greater performance of the proposed hybrid algorithm for such a complex real-world engineering problem. The obtained results for the latter case are compared with the sine-cosine and symbiotic organisms search algorithms as they are the best performing reported algorithms. The comparisons have demonstrated the superiority of the mAOA algorithm over reported best performing algorithms in terms of PSS design, as well.

Low Latency Aware Fog Nodes Placement in Internet of Things Service Infrastructure

Fog Computing extends storage and computation resources closer to end-devices. In several cases, the Internet of Things (IoT) applications that are time-sensitive require low response time. Thus, reducing the latency in IoT networks is one of the essential tasks. To this end, fog computing is developed with a motive for the data production and consumption to always be within proximity; therefore, the fog nodes must be placed at the edge of the network, which is near the end devices, such that the latency is minimized. The optimal location selection for fog node placement within a network out of a very large number of possibilities, such as minimize latency, is a challenging problem. So, it is a combinatorial optimization problem. Hard combinatorial optimization problems (NP-hard) involve huge discrete search spaces. The fog node placement problem is an NP-hard problem. NP-hard problems are often addressed by using heuristic methods and approximation algorithms. Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore in principle, any metaheuristic can be used to solve them. To resolve this, meta-heuristic-based methods is proposed. We apply the Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to design fog node placement algorithms. Genetic Algorithm is observed to give better solutions. Since Genetic Algorithm may get stuck in local optima, Hybrid Genetic Algorithm, and Simulated Annealing (GA-SA), Hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) were compared with GA. By extensive simulations, it is observed that hybrid GA-SA-based for node placement algorithm outperforms other baseline algorithms in terms of response time for the IoT applications.

Defect-free interface between amorphous (Al2O3)1−x(SiO2)x and GaN(0001) revealed by first-principles simulated annealing technique

We report first-principles molecular dynamics (MD) simulations that unveil the interface structures of amorphous mixed oxide (Al2O3)1−x(SiO2)x and GaN polar surfaces. The MD allows us to perform the melt and quench (simulated annealing) simulations to forge distinct amorphous samples. We find that the dangling bonds are completely absent at all the obtained interfaces. This annihilation is due to the diffusion of appropriate species, O for (Al2O3)1−x(SiO2)x/GaN(0001) and Al and Si for (Al2O3)1−x(SiO2)x/GaN(0001¯), from the amorphous to the interface and the subsequent formation of strong bonds with both ionicity and covalency at the interface. This absence of the dangling bond indicates the superiority of (Al2O3)1−x(SiO2)x films to Al2O3 or SiO2 as a gate oxide for the GaN–metal–oxide–semiconductor field effect transistor.

Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory

The problem of misclassification has always been a major concern in detecting online credit card fraud in e-commerce systems. This concern greatly poses a significant challenge to financial institutions and online merchants with regards to financial loss. This paper specifically compares an Artificial Neural Network trained by the Simulated Annealing technique (SA-ANN) with a proposed emerging online learning technology in anomaly detection known as the Hierarchical Temporal Memory based on the Cortical Learning Algorithms (HTM-CLA). Comparisons are also made with a deep recurrent neural technique based on the Long Short-Term Memory ANN (LSTM-ANN). The performances of these systems are investigated on the basis of correctly classifying credit card fraud (CCF) using an average classification performance ratio metric. The results of simulations on two CCF benchmark datasets (the Australian and German CCF data) showed promising competitive performance of the proposed HTM-CLA with the SA-ANN. The HTM-CLA also clearly outperformed the LSTM-ANN in the considered benchmark datasets by a factor of 2:1.

A new approach for finite element based reliability evaluation of offshore corroded pipelines

Internal corrosion is recognized as a serious treat in offshore pipeline services which are supposed to transport vast quantities of oil and natural gases over many long and complicated subsea routes. This paper aims to present a new approach for reliability evaluation of pipelines against corrosion using finite element simulation. In this process the effective random variables are selected by an efficient sensitivity analysis named Tornado Diagram Analysis. Afterwards, a combination of Latin Hypercube Sampling method along with Simulated Annealing technique is used to generate the random simulations which represent the existent epistemic uncertainties. Each realization of this simulation matrix is introduced into the numerical model as the necessary inputs to evaluate the probabilistic responses of the corroded pipeline from linear behavior to failure limit state. This methodology is implemented on a sample offshore pipeline and its results indicate that the maximum Von-Mises stress at all the internal pressure levels follows lognormal distribution. Additionally, as per the findings of this study, the deterministic analysis appears to predict the internal pressure corresponding to the failure limit state conservatively and, therefore, may lead to less remaining life and capacity for corroded pipelines. According to the estimated failure fragility curve, the failure probability of the aforementioned internal pressure level is around 65%.

A novel improved atom search optimization algorithm for designing power system stabilizer

A novel hybrid algorithm developed by merging atom search optimization and simulated annealing algorithms is presented. The constructed improved algorithm, named as improved atom search optimization algorithm, was proposed for optimizing a power system stabilizer adopted in a single-machine infinite-bus power system. The evaluations were initially performed using several benchmark functions by comparing the results with genetic algorithm, simulated annealing technique, particle swarm optimization, gravitational search algorithm and the original version of atom search optimization algorithm. The obtained results showed the great promise of the developed hybrid algorithm in terms of the balance between exploration and exploitation phases. The performance of the proposed hybrid algorithm was also assessed through designing an optimally performing power system stabilizer for further evaluation. To do so, a power system stabilizer damping controller was formulated as an optimization problem and the improved algorithm was used to search for optimal controller parameters in order to show the applicability and greater performance of the proposed algorithm for such a complex real-world engineering problem. The obtained results for the latter case were compared with the best performing reported approaches of sine cosine algorithm and symbiotic organisms search algorithm. The comparisons clearly demonstrated the superiority of the proposed algorithm over other recently reported best performing algorithms for power system stabilizer design.

Energy Efficient Disjoint Multipath Routing Protocol Using Simulated Annealing in MANET

Existing energy efficient multi path routing protocol in Mobile Ad hoc Network (MANET) are not spatially disjoint and did not consider link availability and queuing delay. Hence an energy efficient disjoint multipath routing protocol using Simulated Annealing technique is proposed in this paper. In this protocol, routing will be performed based on the path cost and link cost metric, which in turn is dependent on the network and node characteristics like link availability, network load, battery drain rate, etc. Thus, the nodes selected for routing will be appropriate and will ensure successful data delivery at the destination. Since this technique takes into consideration all the minute network and node features, it can be assured that most of the network failure situations can be avoided, and thus guaranteeing efficient network operation.

Development of a method to linearize the quadratic assignment problem

The paper presents a new powerful technique to linearize the quadratic assignment problem. There are so many techniques available in the literature that are used to linearize the quadratic assignment problem. In all these linear formulations, both the number of variables and the linear constraints significantly increase. The quadratic assignment problem (QAP) is a well-known problem whereby a set of facilities are allocated to a set of locations in such a way that the cost is a function of the distance and flow between the facilities. In this problem, the costs are associated with a facility being placed at a certain location. The objective is to minimize the assignment of each facility to a location. There are three main categories of methods for solving the quadratic assignment problem. These categories are heuristics, bounding techniques and exact algorithms. Heuristics quickly give near-optimal solutions to the quadratic assignment problem. The five main types of heuristics are construction methods, limited enumeration methods, improvement methods, simulated annealing techniques and genetic algorithms. For every formulated QAP, a lower bound can be calculated. We have Gilmore-Lawler bounds, eigenvalue related bounds and bounds based on reformulations as bounding techniques. There are four main classes of methods for solving the quadratic assignment problem exactly, which are dynamic programming, cutting plane techniques, branch and bound procedures and hybrids of the last two. The QAP has application in computer backboard wiring, hospital layout, dartboard design, typewriter keyboard design, production process, scheduling, etc. The technique proposed in this paper has the strength that the number of linear constraints increases by only one after the linearization process.

A two state model study of photo‐detachment dynamics driven by an optimally designed polychromatic field: A simulated annealing based optimisation

AbstractThe surfaces of and F2 are two well separated surfaces modeled by two Morse potentials. In presence of a field, these two surfaces couple and transition from to F2 takes place resulting in the detachment of an electron. We have applied Simulated Annealing (SA) technique to design the polychromatic field which results in ∼ 95% probability of photo detachment (Pd). Also we have designed a field to achieve photo detachment followed by dissociation of FF bond with nearly 38% dissociation probability. F2 being a homo nuclear diatomic molecule, promotion to the continuum states by directly exciting it from its ground vibrational state is an impossibility and our method appears to be a feasible solution if the target is to break the FF bond.

Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

A novel approach for solving travelling thief problem using enhanced simulated annealing.

Real-world optimization problems are getting more and more complex due to the involvement of inter dependencies. These complex problems need more advanced optimizing techniques. The Traveling Thief Problem (TTP) is an optimization problem that combines two well-known NP-Hard problems including the 0/1 knapsack problem and traveling salesman problem. TTP contains a person known as a thief who plans a tour to collect multiple items to fill his knapsack to gain maximum profit while incurring minimum cost in a standard time interval of 600 s. This paper proposed an efficient technique to solve the TTP problem by rearranging the steps of the knapsack. Initially, the picking strategy starts randomly and then a traversal plan is generated through the Lin-Kernighan heuristic. This traversal is then improved by eliminating the insignificant cities which contribute towards profit adversely by applying the modified simulated annealing technique. The proposed technique on different instances shows promising results as compared to other state-of-the-art algorithms. This technique has outperformed on a small and medium-size instance and competitive results have been obtained in the context of relatively larger instances.

РЕШЕНИЕ ПРЯМОЙ ЗАДАЧИ ГРАВИРАЗВЕДКИ МЕТОДОМ ИМИТАЦИИ ОТЖИГА ДЛЯ ИНТЕРПРЕТАЦИИ ДАННЫХ ГРАВИМЕТРИЧЕСКОГО МОНИТОРИНГА СОСТОЯНИЯ НЕДР

The long-term development of hydrocarbon reservoirs (HCR) in the geological environment, complex deformation processes occur. Gravity monitoring is carried out to evaluate the possible geodynamic risk and negative consequences from HCR. As a result, the interrelationships of the continuously changing field-geological situation (changes in production volumes, changes in reservoir pressure, processes of fluid injection into productive formations) are investigated. The main tool for solving the gravity inversion when determining areas of increased industrial hazard is the solution of the gravity direct problem. In these studies, proceeding from a given initial approximation of the environment, the problem is realized through successive approximations. To assess such distributions, the authors of the article recommend using the simulated annealing technique within the framework of stochastic optimization. It is aimed at fitting the optimal parameters of the medium provided that a minimal residual of the gravity field values occurs. The approach is implemented using three simple mathematical models of the geological medium such as horizontal prism, homogeneous sphere, and vertical ledge. This technique allows fitting the media values simultaneously by a pair of its parameters. The operation of the algorithm is described and the simulation results are provided. The results showed acceptable accuracy of the algorithm for solving the direct gravity problem by the proposed method. The simulated annealing technique made it possible to increase the reliability of the HCR model while reduce the time for the analysis of the gravity field.

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

It is widely accepted that the integration of natural sources in distribution networks is becoming more attractive as they are sustainable and nonpolluting. This paper firstly proposes a modified Manta Ray Foraging Optimizer (MMRFO) to enhance the characteristic of MRFO technique. The modified MRFO technique is based on inserting the Simulated Annealing technique into the original MRFO to enhance the exploitation phase which is responsible for finding the promising region in the search area. Secondly, the developed technique is utilized for determining the best sizes and locations of multiple wind turbine (WT) and photovoltaic (PV) units in Radial Distribution System (RDS). The total system loss is taken as single-objective function to be minimized, considering the probabilistic nature of PV and WT output generation with variable load demand. Reactive loss sensitivity factor (QLSF) is utilized for obtaining the candidate locations up to fifty percent of total system buses with the aim of reducing the search space. Battery Energy Storage System (BESS) is used with PV to change it into a dispatchable supply. The changes in system performance by optimally integrating PV and WT alone or together are comprehensively studied. The proposed solution approach is applied for solving the standard IEEE 69 bus RDS. The obtained results demonstrate that installing PV and WT simultaneously achieves superior results than installing PV alone and WT alone in RDS. Further, simultaneous integration of WT and PV with BESS gives better results than simultaneous integration of WT and PV without BESS in RDS. The simulation results prove that the total system losses can be reduced by enabling the reactive power capability of PV inverters. The convergence characteristic shows that the modified MRFO gives the best solutions compared with the original MRFO algorithm.