• Wind and Structures
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• v.29 no.2
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• pp.99-110
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• 2019

This paper provides a simple numerical method to determine the optimal parameters of tuned mass damper (TMD) and viscoelastic dampers (VEDs) in frame structure for wind vibration control considering the soil-structure interation (SSI) effect in frequency domain. Firstly, the numerical model of frame structure equipped with TMD and VEDs considering SSI effect is established in frequency domain. Then, the genetic algorithm (GA) is applied to obtain the optimal parameters of VEDs and TMD. The optimization process is demonstrated by a 20-storey frame structure supported by pile group for different soil conditions. Two wind resistant systems are considered in the analysis, the Structure-TMD system and the Structure-TMD-VEDs system. The example proves that this method can quickly determine the optimal parameters of energy dissipation devices compared with the traditional finite element method, thus is practically valuable.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.881-886
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• 1993

Fuzzy logic rule-based controller has many desirable advantages, which are simple to implement on the real time and need not the information of structure and dynamic characteristics of the system. Thus, nowadays, the scope of the application of the fuzzy logic controller becomes enlarged. But, if the controlled plant is a time-varying and nonlinear system, it is not easy to construct the fuzzy logic rules which usually need the knowledge of an expert. In this paper, an approach in which the logic control rules can be self-organized using genetic algorithm will be proposed and the effectiveness of the proposed method will be verified by computer simulation of the 2 d.o.f. planar robot manipulator.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.4
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• pp.9-16
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• 2010

In the health monitoring of civil engineering structures, the optimal sensor placement has a major influence on the quality of the results. This paper considers the problem of locating sensors with the aim of maximizing the data information so that structural parameters or damage of structures can be assessed. An proposed technique using a genetic algorithm is introduced to find the optimal placement of sensors. The sensitivity on modal vectors by structural parameters and the orthogonality of modal vectors have been taken as the fitness function of the genetic algorithm. A simple tower structure is used for example analyses to investigate the feasibility and applicability of the proposed approach. The example analyses show the way how the modal sensitivity and the modal orthogonality in the fitness function have influence on the optimal sensor placement. It is shown that the present method using the proposed fitness function can provide the reliable results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1215-1227
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• 2002

This paper presents a genetic algorithmic approach to shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation that exchanges partial chromosomes (partial-routes) at positionally independent crossing sites and the mutation operation maintain the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network sizes and topologies) for almost all source-destination pairs.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.407-417
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• 2003

Enen though several GA-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, GA-based optimization methods are computationally too expensive for practical use in the field of structural optimization, particularly for large- scale problems. Furthermore, a successful implementation of GA-based optimization algorithm requires a cumbersome and trial-and-error routine related to setting of parameters dependent on a optimization problem. Therefore, to overcome these disadvantages, a high-performance GA is developed in the form of distributed hybrid genetic algorithm for structural optimization on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consist of a simple GA running on a master computer and multiple μ-GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computational time required for structural optimization process can be drastically reduced and the dependency on the parameters can be avoided.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.455-462
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• 2016

Recently, followed by rapid growth of robotics field, multi-linkage mechanism which can even pass by rough road is getting lots of attention. In this paper, I focused on Jansen mechanism. It's a kinematics object which is named after Dutch artist Theo jansen. Jansen mechanism embraces structure and mechanism which creates locomotion with the combination of the power and simple structure. Theo jansen suggests a 'Holy number'. It's an ideal ratio of leg components length. However, if there's desired gait locomotion, you have to adjust the ratio and the length. But even slight change of the length could cause a big change at the end-point. To solve this problem, I suggest a reverse engineering method to get a ratio of each links by nonlinear optimization with pre-set desired trajectory. First, we converted a movement of the joint of Jansen mechanism to vectors by kinematics analysis of multi-linkage structure. And we showed the trajectory at the end-point. After that, we set desired trajectory which we found most ideal. Then we got the length of the leg components which draws a trajectory as same as trajectory we set, using Multi-objective genetic algorithm toolbox in MATLAB. Result is verified by Edison designer and mSketch. And we analyzed if it could pass through the obstruction which is set dynamically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.362-368
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• 2018

Making proper judgements in urgent situations facing a submarine at the sea is very critical. This is because the commander's misjudgments could drive the entire ally to destruction in a moment. In order to generate appropriate tactics on behalf of the human commander and to analyze the effectiveness in such emergency situations, studies using intelligent agents and genetic algorithms have been conducted. In this study, inference engine based intelligent agent is adopted to each warship and submarine to generate optimal tactics on the variable environment with genetic algorithms. And we analyze the risk of the alliance according to the performance of the enemy submarine through a simple simulation and generate appropriate tactics using the genetic algorithm. Also generated tactics are evaluated and the results are analyzed to figure out why such results are formed.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.109-121
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• 2007

This paper focuses on algorithms based on the evolution, which is applied to various optimization problems. Especially, among these algorithms based on the evolution, we investigate the simple genetic algorithm based on Darwin's evolution, the Lamarckian algorithm based on Lamark's evolution and the Baldwin algorithm based on the Baldwin effect and also Investigate the difference among them in the biological and engineering aspects. Finally, through this comparison, we suggest a new algorithm to find more various solutions changing the genotype or phenotype search space and show the performance of the proposed method. Conclusively, the proposed method showed superior performance to the previous method which was applied to the constrained minimum spanning tree problem and known as the best algorithm.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.19-27
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• 2009

This paper presents a newly developed optimization algorithm, the Bee Optimization Algorithm (BeeOA), to solve the economic power dispatch (EPD) problem. The authors have developed a derivative free and global optimization technique based on the working of the honey bee. The economic power dispatch problem is a nonlinear constrained optimization problem. Classical optimization techniques fail to provide a global solution and evolutionary algorithms provide only a good enough solution. The proposed approach has been examined and tested on two test systems with different objectives. A simple power dispatch problem is tested first on 6 generators and then the algorithm is demonstrated on 13 thermal unit systems whose incremental fuel cost function takes into account the value point loading effect. The results are promising and show the effectiveness and robustness of the proposed approach over recently reported methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.