• Journal of Korean Association for Spatial Structures
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• v.4 no.2 s.12
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• pp.89-97
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• 2004

The objective of this study is the development of a scheme and discrete optimum design algorithm, which is based on the genetic algorithm. The algorithm can perform both scheme and size optimum designs of plane trusses. The developed Scheme genetic algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of structures and the constraints are limits on loads and serviceability. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. However, its application to the complicated structures has been limited because of the extreme time need for a number of structural analyses. This study solves the problem by introducing the size & scheme genetic algorithm operators into the genetic algorithm. The genetic process virtually takes no time. However, the evolutionary process requires a tremendous amount of time for a number of structural analyses. Therefore, the application of the genetic algorithm to the complicated structures is extremely difficult, if not impossible. The scheme genetic algorithm operators was introduced to overcome the problem and to complement the evolutionary process. It is very efficient in the approximate analyses and scheme and size optimization of plane trusses structures and considerably reduces structural analysis time. Scheme and size discrete optimum combined into the genetic algorithm is what makes the practical discrete optimum design of plane fusses structures possible. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying the algorithm to various optimum design examples: plane pratt, howe and warren truss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2574-2587
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• 2019

Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

• Journal of KIISE
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• v.43 no.2
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• pp.204-211
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• 2016

In this paper, we propose an automatic melody composition system that can generate a sophisticated melody by adding non-harmony tone in the given chord progression. An overall procedure consists of two steps, which are the rhythm generation and melody generation parts. In the rhythm generation part, we designed new fitness functions for rhythm that can be controlled by a user setting parameters. In the melody generation part, we designed new fitness functions for melody based on harmony theory. We also designed evolutionary operators that are conducted by considering a musical context to improve computational efficiency. In the experiments, we compared four metaheuristics to optimize the rhythm fitness functions: Simple Genetic Algorithm (SGA), Elitism Genetic Algorithm (EGA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, we compared proposed genetic algorithm for melody with the four algorithms for verifying performance. In addition, composition results are introduced and analyzed with respect to musical correctness.

• Korean Management Science Review
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• v.14 no.2
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• pp.47-61
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• 1997

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.47-47
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• 1989

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.225-228
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• 2001

DNA computing has been applied to the problem of getting an optimal solution 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 for finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms (GA). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses a tool of calculation or Information store with DNA molecules and four-type bases denoted by the symbols of A(Ademine), C(Cytosine), G(Guanine) and T(Thymine). The same operators, selection, crossover, mutation, are applied to the both DNA coding algorithm and genetic algorithms. The results show that the DNA based algorithm performs better than GA.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.123-128
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• 1995

A classical PID controller is designed by applying the GA (Genetic Algorithm) which searches the optimal parameters through three major operators of reproduction, crossover and mutation under the given constraints. The GA could minimize the designer's interference and the whole design process could easily be automated. In contrast with other traditional PID design methods which allows for the system output responses only, the design with the GA can take account of the magnitude or the rate of change of control input together with the output responses, which reflects the more realistic situations. Compared with other PIDs designed by the traditional methods such as Ziegler and analytic, the PID by the GA shows the superior response characteristics to those of others with the least control input energy.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.114-120
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• 1999

본 연구는 Heuristic 알고리즘 및 유전자알고리즘(GA)을 이용하여 3단계의 통합차량운송계획 모델의 개발이다. 차량경로문제(VRP : Vehicle Routing Problem)를 해결하기 위한 접근방법으로 기존의 Saving 알고리즘을 개선하여 사용하였으며 유전자 알고리즘(Genetic Algorithm)의 각종 연산자 (Operators)들을 계산하여 사용하였다. 본 모델은 다음 3단계의 접근방법을 사용하였다 ; 1) 다 물류 센터의 문제해결을 위한 영역활당(Sector Clustering) 모델, 2) 경로계획모델(VRP Model), 및 3) 최적 운송계획모델(GA-TSP Model). 본 모델들을 다양한 운송환경에서, 거리산정방법, 가용운송장비 대수, 운송시간의 제한, 물류센터 및 운송지점의 위치 및 수요량 등 다양한 파라메터들을 고려한 통합시스템으로 3개의 Component로 구성된 GUI-Type 프로그램을 개발하고 Sample 응용결과를 보였으며 기존의 모델들 보다 우수한 결과를 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.101-111
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• 2009

This paper considers two-sided assembly line balancing with preemptive multiple goals. In the problem, three goals are taken into account in the following priority order : minimizing the number of mated-stations, achieving the goal level of workload smoothness, and maximizing the work relatedness. An evolutionary algorithm is used to solve the multiple goal problems. A new structure is presented in the algorithm, which is helpful to searching the solution satisfying the goals in the order of the priority. The proper evolutionary components such as encoding and decoding method, evaluation scheme, and genetic operators, which are specific to the problem being solved, are designed in order to improve the algorithm's performance. The computational results show that the proposed algorithm is premising in the solution quality.