• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.1
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• pp.74-86
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• 2014

The optimization techniques are explored in the direction of arrival (DOA) estimation based on single acoustic pressure gradient vector sensor (APGVS). By analyzing the working principle and measurement errors of the APGVS, acoustic intensity approaches (AI) and the minimum variance distortionless response beamforming approach based on single APGVS (VMVDR) are deduced. The radius to wavelength ratio of the APGVS must be not bigger than 0.1 in the actual application, otherwise its DOA estimation performance will degrade significantly. To improve the robustness and estimation performance of the DOA estimation approaches based on single APGVS, two modified processing approaches based on single APGVS are presented. Simulation and lake trial results indicate that the performance of the modified approaches based on single APGVS are better than AI and VMVDR approaches based on single APGVS when the radius to wavelength ratio is not bigger than 0.1, and the two modified DOA estimation methods have excellent estimation performance when the radius to wavelength ratio is bigger than 0.1.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.17-19
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• 2014

In this paper, we consider an interesting variant of the inverse minimum traveling salesman problem. Given an instance (G, w) of the minimum traveling salesman problem defined on a metric space, we fix a specified Hamiltonian cycle $HC_0$. The task is then to adjust the edge cost vector w to w' so that the new cost vector w' satisfies the triangle inequality condition and $HC_0$ can be returned by the minimum spanning tree algorithm in the TSP-instance defined with w'. The objective is to minimize the total deviation between the original and the new cost vectors with respect to the $L_1$-norm. We call this problem the inverse metric traveling salesman problem against the minimum spanning tree algorithm and show that it is closely related to the inverse metric spanning tree problem.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.1
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• pp.124-131
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• 2005

This paper suggests LOS(Line-Of-Sight) guidance system of a surface vessel in straight-line navigation under ocean currents An LOS vector from the vessel to a point on the path between two way-points is decided and a heading angle is calculated to converge to follow the desired path based on the LOS vector This guidance system is called LOS guidance system. The suggested LOS guidance law has parameters to be properly chosen according to navigational environment. Parameters of LOS guidance system are optimized to reduce propulsive energy and/or position error between desired Position and present position of a ship using genetic algorithm which is a strong optimization algorithm with adaptational random search The effectiveness of the suggested LOS guidance system is assured through computer simulations.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.207-213
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• 2021

Networks in Mobile ad hoc contain distribution and do not have a predefined structure which practically means that network modes can play the role of being clients or servers. The routing protocols used in mobile Ad-hoc networks (MANETs) are characterized by limited bandwidth, mobility, limited power supply, and routing protocols. Hybrid routing protocols solve the delay problem of reactive routing protocols and the routing overhead of proactive routing protocols. The Ant Colony Optimization (ACO) algorithm is used to solve other real-life problems such as the travelling salesman problem, capacity planning, and the vehicle routing challenge. Bio-inspired methods have probed lethal in helping to solve the problem domains in these networks. Hybrid routing protocols combine the distance vector routing protocol (DVRP) and the link-state routing protocol (LSRP) to solve the routing problem.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.5
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• pp.483-491
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• 2015

This paper describes the design optimization of parasitic elements used for TACAN broadband antenna. We deployed parasitic elements arranged in a circular array to electronically rotate the antenna instead of employing a mechanically rotated antenna to generate the composite radiation pattern of 15 Hz and 135 Hz including bearing information and to meet the harmonic contents specification of MIL-STD-291C. We performed the simulation for optimization of the parasitic elements and fabricated the antenna composed of 16 parasitic elements of 15 Hz and 63 parasitic elements of 135 Hz. With harmonics magnitude reduction by increasing the number of steps using vector composition of the reflectors, the measured result meets the specification of MIL-STD-291C.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.495-504
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• 2013

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Structural Engineering and Mechanics
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• v.62 no.5
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.