• IE interfaces
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• v.20 no.4
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• pp.504-514
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• 2007

The multicast routing problem lies in the composition of a multicast routing tree including a source node and multiple destinations. There is a trade-off relationship between cost and delay, and the multicast routing problem of optimizing these two conditions at the same time is a difficult problem to solve and it belongs to a multi-objective optimization problem (MOOP). A multi-objective genetic algorithm (MOGA) is efficient to solve MOOP. A micro-genetic algorithm(${\mu}GA$) is a genetic algorithm with a very small population and a reinitialization process, and it is faster than a simple genetic algorithm (SGA). We propose a multi-objective micro-genetic algorithm (MO${\mu}GA$) that combines a MOGA and a ${\mu}GA$ to find optimal solutions (Pareto optimal solutions) of multicast routing problems. Computational results of a MO${\mu}GA$ show fast convergence and give better solutions for the same amount of computation than a MOGA.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1041-1062
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• 2015

In this paper a method of finding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and $H_{\infty}$ norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

• Journal of the Korea Industrial Information Systems Research
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• v.17 no.2
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• pp.57-64
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• 2012

In this paper, the multi-objective genetic algorithm is proposed for the document clustering which is important in the text mining field. The most important function in the document clustering algorithm is to group the similar documents in a corpus. So far, the k-means clustering and genetic algorithms are much in progress in this field. However, the k-means clustering depends too much on the initial centroid, the genetic algorithm has the disadvantage of coming off in the local optimal value easily according to the fitness function. In this paper, the multi-objective genetic algorithm is applied to the document clustering in order to complement these disadvantages while its accuracy is analyzed and compared to the existing algorithms. In our experimental results, the multi-objective genetic algorithm introduced in this paper shows the accuracy improvement which is superior to the k-means clustering(about 20 %) and the general genetic algorithm (about 17 %) for the document clustering.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.8
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• pp.689-696
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• 2014

This paper presents a scheduling problem for a high-density robotic workcell using multi-objective genetic algorithm. We propose a new algorithm based on NSGA-II(Non-dominated Sorting Algorithm-II) which is the most popular algorithm to solve multi-objective optimization problems. To solve the problem efficiently, the proposed algorithm divides the problem into two processes: clustering and scheduling. In clustering process, we focus on multi-robot positions because they are fixed in manufacturing system and have a great effect on task distribution. We test the algorithm by changing multi-robot positions and compare it to previous work. Test results shows that the proposed algorithm is effective under various conditions.

• 한국가스학회:학술대회논문집
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• pp.115-120
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• 1997

• Steel and Composite Structures
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• v.15 no.4
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• pp.451-466
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• 2013

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

• Ocean Systems Engineering
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• v.5 no.2
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• pp.91-107
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• 2015

Ship hull optimization is categorized as a bound, multi variable, multi objective problem with nonlinear constraints. In such analysis, where the objective function representing the performance of the ship generally requires computationally involved hydrodynamic interaction evaluation methods, the objective functions are not smooth. Hence, the evolutionary techniques to attain the optimum hull forms is considered as the most practical strategy. In this study, a parametric ship hull form represented by B-Spline curves is optimized for multiple performance criteria using Genetic Algorithm. The methodology applied to automate the hull form generation, selection of optimization solvers and hydrodynamic parameter calculation for objective function and constraint definition are discussed here.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.486-495
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• 2013

In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensionalGA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objective optimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.

• Journal of Korean Association for Spatial Structures
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• v.13 no.2
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• pp.37-45
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• 2013

Reduction of microvibration is regarded as important in high-technology facilities with high precision equipments. In this paper, smart control technology is used to improve the microvibration control performance. Mr damper is used to make a smart base isolation system amd fuzzy logic control algorithm is employed to appropriately control the MR damper. In order to develop optimal fuzzy control algorithm, a multi-objective genetic algorithm is used in this study. As an excitation, a train-induced ground acceleration is used for time history analysis and three-story example building structure is employed. Microvibration control performance of passive and smart base isolation systems have been investigated in this study. Numerical simulation results show that the multi-objective genetic algorithm can provide optimal fuzzy logic controllers for smart base isolation system and the smart control system can effectively reduce microvibration of a high-technology facility subjected to train-induced excitation.