scholarly journals Search for Global Maxima in Multimodal Functions by Applying Numerical Optimization Algorithms: A Comparison between Golden Section and Simulated Annealing

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 43 ◽  
Author(s):  
Jordan Guillot ◽  
Diego Restrepo-Leal ◽  
Carlos Robles-Algarín ◽  
Ingrid Oliveros
Keyword(s):  
Simulated Annealing ◽  
Numerical Optimization ◽  
Simulated Annealing Algorithm ◽  
Golden Section ◽  
Optimization Algorithms ◽  
Computation Time ◽  
Photovoltaic Module ◽  
Test Scenario ◽  
Partial Shading ◽  
Golden Section Search

In the field of engineering when a situation is not resolved analytically, efforts are made to develop methods that approximate a possible solution. These efforts have originated the numerical methods known at present, which allow formulating mathematical problems that can be solved using logical and arithmetic operations. This paper presents a comparison between the numerical optimization algorithms golden section search and simulated annealing, which are tested in four different scenarios. These scenarios are functions implemented with a feedforward neural network, which emulate a partial shading behavior in photovoltaic modules with local and global maxima. The presence of the local maxima makes it difficult to track the maximum power point, necessary to obtain the highest possible performance of the photovoltaic module. The programming of the algorithms was performed in C language. The results demonstrate the effectiveness of the algorithms to find global maxima. However, the golden section search method showed a better performance in terms of percentage of error, computation time and number of iterations, except in test scenario number three, where a better percentage of error was obtained with the simulated annealing algorithm for a computational temperature of 1000.

The Application and Comparison of Two Different Optimization Algorithms in the Optimization Design of Hydrofoil

2012 ◽  
Vol 178-181 ◽  
pp. 2871-2876
Author(s):  
Chao Wang ◽  
Feng Feng ◽  
Xin Chang ◽  
Chun Yu Guo ◽  
Yang Hao Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Design ◽  
Simulated Annealing Algorithm ◽  
Optimization Algorithms ◽  
Optimization Method ◽  
Flow Solver ◽  
Propeller Design ◽  
Section Design ◽  
Annealing Algorithm

Hydrofoil is the important part of ship design and diverse motion equipment. The optimization design of hydrofoil section on lift-to-drag radio with genetic algorithm (GA) and simulated annealing algorithm are demonstrated, and the method on the hydrofoil section design of the propeller design will be done. Objective function and fitness of every individual are provided by flow solver of panel method. The optimization method on design of hydrofoil section on lift-to-drag is successfully used. The optimization results show the combination of optimization algorithm is feasible at the optimal design of hydrofoil sections. What’s more, a comparison between two different optimization algorithms is made, a conclusion that the simulated annealing algorithm is better then the genetic algorithm is obtained.

scholarly journals Training of ANFIS with simulated annealing algorithm on flexural buckling load prediction of aluminium alloy columns

2020 ◽  
pp. 15-23
Author(s):  
Bulent Haznedar ◽  
Rabia Bayraktar ◽  
Melih Yayla ◽  
Mustafa Diyar Demirkol
Keyword(s):  
Simulated Annealing ◽  
Aluminium Alloy ◽  
Soft Computing ◽  
Simulated Annealing Algorithm ◽  
Optimization Algorithms ◽  
Load Prediction ◽  
Inference System ◽  
Flexural Buckling ◽  
Bee Colony ◽  
Annealing Algorithm

In this study, we propose a simulated annealing algorithm (SA) to train an adaptive neurofuzzy inference system (ANFIS). We performed different types of optimization algorithms such as genetic algorithm (GA), SA and artificial bee colony algorithm on two different problem types. Then, we measured the performance of these algorithms. First, we applied optimization algorithms on eight numerical benchmark functions which are sphere, axis parallel hyper-ellipsoid, Rosenbrock, Rastrigin, Schwefel, Griewank, sum of different powers and Ackley functions. After that, the training of ANFIS is carried out by mentioned optimization algorithms to predict the strength of heat-treated fine-drawn aluminium composite columns defeated by flexural bending. In summary, the accuracy of the proposed soft computing model was compared with the accuracy of the results of existing methods in the literature. It is seen that the training of ANFIS with the SA has more accuracy.   Keywords: Soft computing, ANFIS, simulated annealing, flexural buckling, aluminium alloy columns.

scholarly journals Solution of series-parallel photovoltaic arrays model using global optimization algorithms

2020 ◽  
Vol 25 (1) ◽  
pp. 14-22
Author(s):  
Juan David Bastidas-Rodriguez ◽  
Jorge Mario Cruz-Duarte ◽  
Carlos Rodrigo Correa-Cely
Keyword(s):  
Global Optimization ◽  
Nonlinear Equation ◽  
Nonlinear Equations ◽  
Optimization Problem ◽  
Optimization Algorithms ◽  
Computation Time ◽  
Equation System ◽  
System Of Nonlinear Equations ◽  
Electrical Behavior ◽  
Partial Shading

Models of series-parallel (SP) photovoltaic (PV) arrays focus on the system of nonlinear equations that represents the array’s electrical behavior. The solution of the system of nonlinear equations can be posed as an optimization problem and solved with different methods; however, the models do not formulate the optimization problem and do not evaluate different optimization algorithms for its solution. This paper proposes a solution, using global optimization algorithms, of the mathematical model that describes the electrical behavior of a SP generator, operating under uniform and partial shading conditions. Such a model is constructed by dividing the generator into strings and representing each module in the string with the single-diode model. Consequently, for each string a system of nonlinear equations is build applying the Kirchhoff’s laws, where the unknowns are the modules’ voltages. The solution of the resulting nonlinear equation system is posed as an optimization problem, where the objective function is defined as the sum of the squared of each nonlinear equation. Minimum and maximum values of each voltage are defined from the datasheet information of the modules and bypass diodes. As a demonstrative example, we arbitrarily select two well-known algorithms to solve this problem: Genetic Algorithms and Particle Swarm Optimization. Simulation results show that both algorithms solve the optimization problem and allow the reproduction of the generator’s characteristic curves. Moreover, the results also indicate that the optimization problem is correctly defined, which opens the possibility explore other optimization algorithms to reduce the computation time.

An Improved Simulated Annealing Algorithm for Optimal Design

1995 ◽  
Author(s):  
Sherif Aly ◽  
Madara M. Ogot ◽  
Richard Pelz
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Simulated Annealing Algorithm ◽  
Random Search ◽  
Computation Time ◽  
Global Optimum ◽  
Search Method ◽  
Local Search Method ◽  
Iterative Improvement

Abstract A new algorithm based on the simulated annealing (SA) optimization algorithm is presented. This approach, simulated annealing with random search iterative improvement (SAWI), essentially initiates the SA process to locate the neighborhood of the global optimum. Prior to the convergence of SA, the algorithm switches to random search iterative improvement, a local search method, to converge to the optimum. The key to the effectiveness of SAWI is identifying when the premature termination of SA should occur. This paper presents the results of a parametric study conducted on the transition parameter, illustrating the effects of delayed and premature transition to the local search method, on the final solution. Two examples are presented and discussed to illustrate the efficacy of the algorithm. The results of these examples demonstrate that SAWI makes significant reductions in computation time while maintaining the simplicity of the original SA algorithm and without loss in quality of solution.

Aerodynamic optimization of a SCO2 radial-inflow turbine based on an improved simulated annealing algorithm

2020 ◽  
pp. 095765092097666
Author(s):  
Yuqi Wang ◽  
Yunzhu Li ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Computation Time ◽  
Optimization Method ◽  
Vital Role ◽  
Meridian Plane ◽  
Aerodynamic Optimization ◽  
Compact Structure ◽  
Low Viscosity ◽  
Annealing Algorithm

Supercritical carbon dioxide plays a vital role in the development of power generation applications. It owns the characteristics of high density and low viscosity, which can ensure a compact structure for turbomachinery. With the blossom of optimization algorithm, an interdisciplinary research which applies optimization method to a traditional design process of turbomachinery can accelerate the course and promote the validity by leaps and bounds. We improve the traditional simulated annealing algorithm and establish an optimization process containing the optimization of rotor meridian plane and nozzle profile. This process can effectively reduce the computation time by establishing a surrogate model of coarse mesh simulation. The effects of traditional simulated annealing algorithm (SAA), genetic algorithm (GA) and improve simulated annealing algorithm (ISAA) are compared. As a result, we realize a maximum of 4.94% promotion for isentropic efficiency in ISAA computation. Also, ISAA method saves the computation time by 59.6% compared to GA and by 41.5% compared to SAA. Applying ISAA optimization method to the turbine in a kW-scale solar-driven Brayton cycle power system, we realize a 1.17% increase for the system efficiency.

scholarly journals Multi-Manned Assembly Line Balancing: Workforce Synchronization for Big Data Sets through Simulated Annealing

2021 ◽  
Vol 11 (6) ◽  
pp. 2523
Author(s):  
Francesco Pilati ◽  
Emilio Ferrari ◽  
Mauro Gamberi ◽  
Silvia Margelli
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Assembly Line Balancing ◽  
Task Assignment ◽  
Computation Time ◽  
Mixed Integer ◽  
Solution Quality ◽  
Line Design

The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.

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