scholarly journals Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1297
Author(s):  
Md. Shabiul Islam ◽  
Most Tahamina Khatoon ◽  
Kazy Noor-e-Alam Siddiquee ◽  
Wong Hin Yong ◽  
Mohammad Nurul Huda
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Real Time ◽  
Large Scale ◽  
Fitness Function ◽  
Random Search ◽  
Linear Equations ◽  
Simultaneous Equations ◽  
Function Evaluation ◽  
Solution Sets

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Multi-point optimization of transonic airfoils using an enhanced genetic algorithm

2017 ◽  
Vol 232 (7) ◽  
pp. 1347-1360 ◽  
Author(s):  
Narjes Timnak ◽  
Alireza Jahangirian
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Fitness Function ◽  
Single Point ◽  
Navier Stokes ◽  
Computational Time ◽  
Adaptive Mutation ◽  
Function Evaluation ◽  
New Techniques ◽  
New Methods

In this study, two new techniques are proposed for accelerating the multi-point optimization of an airfoil shape by genetic algorithms. In such multi-point evolutionary optimization, the objective function has to be evaluated several times more than a single-point optimization. Thus, excessive computational time is crucial in these problems particularly, when computational fluid dynamics is used for fitness function evaluation. Two new techniques of preadaptive range operator and adaptive mutation rate are proposed. An unstructured grid Navier–Stokes flow solver with a two-equation [Formula: see text] turbulence model is used to evaluate the objective function. The new methods are applied for optimum design of a transonic airfoil at two speed conditions. The results show that using the new methods can increase the aerodynamic efficiency of optimum airfoil at each operating condition with about 30% less computational time in comparison with the conventional genetic algorithm approach.

scholarly journals Genetic Algorithm Application in Optimization of Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Ali Norouzi ◽  
A. Halim Zaim
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Fitness Function ◽  
Optimization Methods ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Final Formula ◽  
Nonlinear Optimization Methods

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.

A Comparison of Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing in Real-Time Task Scheduling on CMP

2013 ◽  
Vol 679 ◽  
pp. 77-81 ◽  
Author(s):  
Song Chai ◽  
Yu Bai Li ◽  
Chang Wu ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Real Time ◽  
Schedule Problem ◽  
Particle Swarm ◽  
Full Potential ◽  
Swarm Optimization ◽  
Task Schedule ◽  
Time Task

Real-time task schedule problem in Chip-Multiprocessor (CMP) receives wide attention in recent years. It is partly because the increasing demand for CMP solutions call for better schedule algorithm to exploit the full potential of hardware, and partly because of the complexity of schedule problem, which itself is an NP-hard problem. To address this task schedule problem, various of heuristics have been studied, among which, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are the most popular ones. In this paper, we implement these 3 schedule heuristics, and compare their performance under the context of real-time tasks scheduling on CMP. According to the results of our intensive simulations, PSO has the best fitness optimization of these 3 algorithms, and SA is the most efficient algorithm.

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Location Routing ◽  
Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

Wavelength Selection Characterization for NIR Spectra

1997 ◽  
Vol 51 (5) ◽  
pp. 689-699 ◽  
Author(s):  
Jason M. Brenchley ◽  
Uwe Hörchner ◽  
John H. Kalivas
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Least Squares ◽  
Large Scale ◽  
Near Infrared ◽  
Optimization Algorithms ◽  
Principal Component Regression ◽  
Principal Component ◽  
Computer Time ◽  
Operational Parameters

For quantitative analysis of samples based on near-infrared (NIR) spectra, it is common practice to use full spectra in conjunction with partial least-squares (PLS) or principal component regression. Alternatively, least-squares (LS) can be used provided that proper wavelengths have been selected. Recently, optimization algorithms such as simulated annealing and the genetic algorithm have been applied to the selection of individual wavelengths. These algorithms are touted as global optimizers capable of locating the best set of parameters for a given large-scale optimization problem. Optimization methods such as simulated annealing and the genetic algorithm can become time intensive. Excessive computer time may be due not to computations but to the need to determine proper operational parameters to ensure acceptable optimization results. In order to reduce the time to select wavelengths, a different approach consists of selecting wavelengths directly on the basis of spectral criteria. This paper shows that results are not acceptable when one is separately using the criteria of large wavelength correlations to the prediction property, wavelengths associated with large values in loading vectors from PLS or derived from the singular value decomposition (SVD) of the spectra, and wavelengths associated with large PLS regression coefficients. However, it is demonstrated that acceptable results can be produced by using wavelength regions simultaneously associated with large correlations and loading values provided that the level of noise for identified wavelengths is also acceptable. Thus, this paper shows that, rather than using time-consuming optimization algorithms that generally select individual wavelengths, one can achieve improved results based on wavelength windows directly selected. In other words, the described approach is founded on the exclusion of spectral regions rather than the search for distinct wavelengths. As part of the NIR spectral characterization, it is shown that certain loading vectors from the SVD of spectra are equivalent to correlograms for prediction properties. The same is shown to be true for PLS loading vectors. This type of analysis is useful for determining dominant properties of spectra, i.e., primary properties responsible for spectral variations.

Optimum power of a nonlinear piezomagnetoelastic energy harvester with using multidisciplinary optimization algorithms

2020 ◽  
pp. 1045389X2097443
Author(s):  
Mohammad Tahmasbi ◽  
Asghar Jamshiddoust ◽  
Amin Farrokhabadi
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Energy Harvesting ◽  
Energy Harvester ◽  
Random Search ◽  
Electrical Power ◽  
Multidisciplinary Optimization ◽  
Physical Parameters ◽  
Parallel Configuration ◽  
Energy Harvesting Devices

Energy-harvesting devices have been widely used to generate electrical power. Through the use of energy harvesting techniques, ambient vibration energy can be captured and converted into usable electricity in order to create self-powering systems. In the present study, to further improve the efficiency of energy-harvesting devices, a nonlinear piezomagnetoelastic energy harvester is proposed in two different configurations that is parallel and series. In order to optimize the generated electrical power, the physical parameters of the harvester are chosen as the design variables. Classical and Metaheuristic algorithms, namely, random search, genetic algorithm, and simulated annealing are applied to optimize the output power regarding the stress and displacement constraints and feasible variable bounds. Finally, the results of the applied algorithms are compared together. The results demonstrate that most of the implemented algorithms converge to the similar objective function value. The constrained random search methods with SQP and active set algorithms converge faster with small iterations. However, the genetic algorithm and simulated annealing algorithm are more capable to find the global optimum. The obtained results revealed that, before the optimization, the average extracted power in specified time was 3.121 W in parallel configuration and 3.156 W in serial configuration. By using the optimization approaches, the power converged to 4.273 W in parallel configuration and 4.296 W in serial configuration that means the power is increased by 36.9% and 36.1% approximately.

scholarly journals Flight Motion Controller Design using Genetic Algorithm for a Quadcopter

2018 ◽  
Vol 51 (3-4) ◽  
pp. 59-64 ◽  
Author(s):  
Huu Khoa Tran ◽  
Thanh Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Controller Design ◽  
Fitness Function ◽  
Absolute Error ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Time Operation ◽  
Real Time Operation ◽  
Effective Stability

In this study, the Genetic Algorithm operability is assigned to optimize the proportional–integral–derivative controller parameters for both simulation and real-time operation of quadcopter flight motion. The optimized proportional–integral–derivative gains, using Genetic Algorithm to minimum the fitness function via the integral of time multiplied by absolute error criterion, are then integrated to control the quadcopter flight motion. In addition, the proposed controller design is successfully implemented to the experimental real-time flight motion. The performance results are proven that the highly effective stability operation and the reliable of waypoint tracking.

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