• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.179-186
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• 2005

This paper describes a genetic algorithm and compares three crossover operators for Rural Postman Problem with Time Windows (RPPTW). The RPPTW which is a multiobjective optimization problem, is an extension of Rural Postman Problem(RPP) in which some service places (located at edge) require service time windows that consist of earliest time and latest time. Hence, RPM is a m띤tieect optimization Problem that has minimal routing cost being serviced within the given time at each service Place. To solve the RPPTW which is a multiobjective optimization problem, we obtain a Pareto-optimal set that the superiority of each objective can not be compared. This Paper performs experiments using three crossovers for 12 randomly generated test problems and compares the results. The crossovers using in this Paper are Partially Matched Exchange(PMX) Order Exchange(OX), and Modified Order Exchange(MOX) which is proposed in this paper. For each test problem, the results show the efficacy of MOX method for RPPTW.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.1-8
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• 2009

This paper proposes a new document clustering system using fuzzy logic-based genetic algorithm (GA) and semantic vector expansion technology. It has been known in many GA papers that the success depends on two factors, the diversity of the population and the capability to convergence. We use the fuzzy logic-based operators to adaptively adjust the influence between these two factors. In traditional document clustering, the most popular and straightforward approach to represent the document is vector space model (VSM). However, this approach not only leads to a high dimensional feature space, but also ignores the semantic relationships between some important words, which would affect the accuracy of clustering. In this paper we use latent semantic analysis (LSA)to expand the documents to corresponding semantic vectors conceptually, rather than the individual terms. Meanwhile, the sizes of the vectors can be reduced drastically. We test our clustering algorithm on 20 news groups and Reuter collection data sets. The results show that our method outperforms the conventional GA in various document representation environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2269-2275
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• 2010

The performance of Genetic Algorithms (GA) is affected by various factors such as parameters, genetic operators and strategies. The traditional approach with random initial population is efficient however the whole initial population may contain many infeasible solutions. Thus it would take a long time for GA to produce a good solution. The GA have been modified in various ways to achieve faster convergence and it was particularly recognized by researchers that initial population greatly affects the performance of GA. This study proposes modified GA with sorted initial population and applies it to solving Travelling Salesman Problem (TSP). Normally, the bigger the initial the population is the more computationally expensive the calculation becomes with each generation. New approach allows reducing the size of the initial problem and thus achieve faster convergence. The proposed approach is tested on a simulator built using object-oriented approach and the test results prove the validity of the proposed method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2116-2124
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• 2000

In this paper we propose combustion control of refuse incineration plant using fuzzy model and genetic algorithm. At first fuzzy modelling is performed to obtain fuzzy model of the refuse incineration plant and obtained fuzzy model predicts outputs of the plant when inputs are given. Fuzzy model ca be used to obtain control strategy, and train and enhance operators' skill by simulating the plant. Then genetic algorithms search and find out optimal control inputs over all possible solutions in respect to desired outputs and these are inserted to plant. In order to testify proposed control method, computer simulation was carried out. As a result, ISE of fuzzy model of refuse incineration plant is 0.015 and ITAE of control by proposed method, 352 which is better than that by manual operation.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.11a
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• pp.35-39
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• 2004

In a shipyard, there exist various critical decision making components pertaining to various production hindrances. The most prominent one is best-fit spatial arrangement for the minimal spatial occupancy with better pick-ability for the erection of the ship in the dock. During the present research, a concept have been conceived to evade the gap between the identification oj inter-relationships among a set of blocks to be included on a pre-erection area, and a detailed graphical layout of their positions, is called an Optimal Block Relationship Diagram A research has been performed on generation of optimal (or near Optimal) that is, with minimal scrap area. An effort has been made in the generation of optimal (or near-optimal) Optimal Block Relationship Diagram with the Goldberg's Genetic Algorithms with a representation and a set of operators are 'trained' specifically for this application. The expected result to date predicts very good solutions to test problems involving innumerable different blocks to place. The suggested algorithm could accept input from an erection sequence generator program which assists the user in defining the nature and strength of the relationships among blocks, and could produce input suitable for use in a detailed layout stage.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.190-193
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• 2004

Optimal Power Flow (OPF) is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. So this paper presents parallel genetic algorithm-tap search for the solution of the OPF. The control variables modeled unit active power outputs, generator-bus voltage magnitudes and transformer-tap settings. A number of functional operating constraints, such as branch flow limits, load bus boltage magnitude limits and generator reactive capabilities are included as penalties in the fitness function. In parallel GA-TS, GA operators are executed for each process. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper three populations to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on an IEEE 30-bus system in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient for the OPF.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.538-542
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• 2001

DNA computing has been applied to the problem of getting an optimal soluting since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems This paper presents DNA coding method finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms(GA). GA searches efffectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses DNA molecules and four-type bases denoted by the A(Ademine) C(Gytosine);G(Guanine)and T(Thymine). The selection, crossover, mutation operators are applied to both DNA coding algorithm and genetic algorithms and the comparison has been performed. The results show that the DNA based algorithm performs better than GA.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.622-627
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• 2006

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Korean Management Science Review
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• v.30 no.1
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• pp.15-24
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• 2013

We consider the single machine scheduling problem with a rate-modifying activity and time-dependent deterioration after the activity. The class of scheduling problems with rate-modifying activities and the class of scheduling problems with time-dependent processing times have been studied independently. However, the integration of these classes is motivated by human operators of tasks who has fatigue while carrying out the operation of a series of tasks. This situation is also applicable to machines that experience performance degradation over time due to mal-position or mal-alignment of jobs, abrasion of tools, and scraps of operations, etc. In this study, the integration of the two classes of scheduling problems is considered. We present a mathematical model to determine job-sequence and a position of a rate-modifying activity for the integration problem. Since the model is difficult to solve as the size of real problem being very large, we propose genetic algorithms. The performance of the algorithms are compared with optimal solutions with various problems.