Global Search Ability Of Genetic Algorithm Research Articles

Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data

The traditional genetic algorithm (GA) has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters. Therefore, this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA. This paper proposes a genetic Nelder-Mead neural network algorithm (GNMNNA). This algorithm uses a neural network algorithm (NNA) to optimize the global search ability of GA. At the same time, the simplex algorithm is used to optimize the local search capability of the GA. Through numerical examples, the stability of the inversion algorithm under different strategies is explored. The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms. The effectiveness of GNMNNA is verified by the Bodrum–Kos earthquake and Monte Cristo Range earthquake. The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability. Therefore, GNMNNA has greater application potential in complex earthquake environment.

A Comparative Study of Meta-heuristic Algorithms in Supply Chain Networks

Today, with the development of Information Technology (IT) and economic globalization, the suppliers’ selection has been emphasized in supply chain systems. Accordingly, artificial intelligence-based methods have attracted much attention. Hence, in this research, the selection of appropriate suppliers with respect to the multi-resource supply policy, and the implementation of lateral transshipment have been studied, and meta-heuristic algorithms have been employed to solve the problem. In the proposed method, the supply chain network is improved by minimizing the inventory shortages through utilizing lateral transshipment between different factories. In order to efficiently solve the problem, a hybrid meta-heuristic algorithm based on population-based genetic algorithm (GA) and single-solution simulated annealing (SA), named GASA, is propose, in order to simultaneously gain with the advantages of both algorithms, i.e., global search ability of GA and local search ability of SA. In order to compare the results of the proposed GASA, it is compared with GA and SA, to find the best solution. Given the parameters optimization and conducted analyses and comparisons of primary and hybrid algorithms performance, the hybrid GASA algorithm has been identified as the most efficient algorithm to solve the problem,compared to the other algorithms, emphasizing cost reduction and shortage volume.

Structural Optimization of a Steel Arch Bridge with Genetic Algorithm

Structural optimization has become an important tool for structural designers. It helps the designers to find optimal design solutions with better exploitation of materials subject to various constraints. In this article, to face the critical issue of huge horizontal thrust occurred in the Calatrava bridge over the Grand Canal of Venice, single-objective and multi-objective genetic algorithm (GA) based optimization procedures are set up, and the thickness optimization of the bridge is carried out separately with the proposed design procedures and the optimization module implemented in ANSYS. The results are compared in terms of steel volume and horizontal thrust level. It shows that the GA-based design procedures are effective tools to achieve optimal design due to the global search ability of GA. Further, with these powerful tools, more reasonable thickness distributions of steel plates and tubes of the Calatrava bridge can be obtained, meanwhile, the horizontal thrust can be reduced remarkably, and hence the high cost due to the large horizontal thrust of original design could be reduced.

GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor (DSP) is proposed. First, the combination of genetic algorithm (GA) and simulated annealing algorithm (SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

A New Feature Selection Method Based on Ant Colony and Genetic Algorithm on Persian Font Recognition

Dimensionality reduction of a feature set is a usual pre-processing step used for classification applications to improve their accuracy with a small and appropriate feature subset. In this article, a new hybrid of ant colony optimization (ACO) and genetic algorithm (GAs) as a feature selector, and support vector machine as a classifier are integrated effectively. Based on the combination of the fast global search ability of GA and the positive feedback mechanism of ACO, a novel algorithm was proposed in the domain of feature selection. Experiments show that the proposed feature selection can achieve better performance than that the normal GA and ACO does. We tested this method on the extracted features of ten common Persian fonts. The result shows it has affected slightly better on the performance. Furthermore, number of features decreased to almost half of the original feature number after this pre-processing step.

A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling

Parameter identification is the key technology in measurement-based load modeling. A hybrid learning algorithm is proposed to identify parameters for the aggregate load model (ZIP augmented with induction motor). The hybrid learning algorithm combines the genetic algorithm (GA) and the nonlinear Levenberg-Marquardt (L-M) algorithm. It takes advantages of the global search ability of GA and the local search ability of L-M algorithm, which is a more powerful search technique. The proposed algorithm is tested for load parameter identifications using both simulation data and field measurement data. Numerical results illustrate that the hybrid learning algorithm can improve the accuracy and reduce the computation time for load model parameter identifications.

FPGA placement optimization by two-step unified Genetic Algorithm and Simulated Annealing algorithm

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.