This paper proposes a model predictive controller of robot manipulators using a genetic algorithm to secure the best performance by performing parameter optimization with the genetic algorithm. Genetic algorithm is a natural evolutionary process modeled as a computer algorithm and has excellent performance in global optimization, so it is useful for tuning control parameters. The sliding mode controller and inverse dynamics controller are included in the lower part of the model prediction controller to minimize the problems caused by non-linearity and uncertainty of the robot manipulator. The performance superiority of the proposed method as described above has been confirmed in detail through a simulation study.

Distributed genetic algorithm (DGA), also known as island model or coarse-grained model, is a kind of parallel genetic algorithm, in which a population is partitioned into several sub-populations and each of them evolves with its own genetic operators to maintain diversity of individuals. It is known that DGA is superior to conventional genetic algorithm with a single population in terms of solution quality and computation time. Several researches have been conducted to evaluate effects of parameters on GAs, but there is no research work yet that deals with structure of DGA. In this study, we tried to evaluate performance of various genetic algorithms (GAs) for the famous symmetric traveling salesman problems. The considered GAs include a conventional serial GA (SGA) with IGX (Improved Greedy Crossover) and several DGAs with various combinations of crossover operators such as OX (Order Crossover), DPX (Distance Preserving Crossover), GX (Greedy Crossover), and IGX. Two distinct immigration policies, conventional noncompetitive policy and newly proposed competitive policy are also considered. To compare performance of GAs clearly, a series of analysis of variance (ANOVA) is conducted for several scenarios. The experimental results and ANOVAs show that DGAs outperform SGA in terms of computation time, while the solution quality is statistically the same. The most effective crossover operators are revealed as IGX and DPX, especially IGX is outstanding to improve solution quality regardless of type of GAs. In the perspective of immigration policy, the proposed competitive policy is slightly superior to the conventional policy when the problem size is large.

This paper considers an economic design problem of a tree-based network which is a kind of computer network. This problem can be modeling to be an objective function to minimize installation costs, on the constraints of spanning tree and maximum traffic c

유전자 알고리즘을 이용하여 구조물의 지진응답해석에 사용할 인공 가속도시간이력을 작성하는 방법을 제시한다. 유전자 알고리즘을 적용하기 위해서 유전원질에 해당되는 결정변수로서 응답스펙트럼 값을 계산할 진동수를 결정하고, 산술평균 교차연산자와 산술비 돌연변이연산자를 제안한다. 이들 연산자와 전형적인 단순 교차연산자를 사용하여 설계응답스펙트럼에 부합하는 인공 지진파 작성에 사용한다. 또한 작성된 인공 가속도시간이력은 실제 계측되는 지진파의 몇 가지의 외형적 특성을 가져야 하므로 이를 고려한 인공 가속도시간이력이 작성되도록 한다. 이 외형적 특성으로는 가속도시간이력의 포락형태, 지진파의 2수평성분간의 상관관계, 지반의 최대가속도 - 최대속도 - 최대변위 관계 등이다.

  This paper considers a topological optimization of a computer network design with a cost constraint. The objective is to find the topological layout of links, at maximal reliability, under the constraint that the network cost is less or equal than a giv

교통량, 속도, 차종 등으로 대표되는 교통자료는 도로를 계획하고 설계하는데 있어 매우 중요한 기초자료로 활용된다. 교통자료를 기준으로 해당 도로의 장래 서비스수준을 예측하며, 신설 및 확장될 도로의 기하구조가 결정되기 때문이다. 1985년 이후부터 건설교통부에서는 일반국도에 대해서 수시 교통량 조사와 상시 교통량 조사를 병행하고 있다. 이러한 교통조사는 일반국도와 일반국도 또는 일반국도와 고속국도가 만나는 네트웍 상의 노드를 중심으로 교통조사 구간을 설정하고, 이들 교통조사 구간에 대해서 교통량 조사를 수행하고 있다. 이러한 교통조사구간 설정 방법은 주요 도로가 만나는 결절점 사이의 구간에서는 교통량 변화패턴이 유사하다는 것을 전제로 하고 있다. 최근 우회도로의 신설, 중앙분리대 설치 등의 도로 기하구조 및 교통 시설물의 설치로 인하여 기존 구간의 특성이 변화되었다. 따라서 전국 일반국도를 대상으로 교통조사 구간의 유사성을 평가하여 국도의 동질성 구간에 대한 분석을 수행하였다. 유사성 평가를 위해서는 유전자 알고리즘을 적용한 모형을 구축하고, 모형의 적용을 통해 교통조사 구간을 정의하였다.

This paper investigated the optimum design of truss structures based on Genetic Algorithms (GA's). With GA's characteristic of running side by side, the overall optimization and feasible operation, the optimum design model of truss structures was established. Elite models were used to assure that the best units of the previous generation had access to the evolution of current generation. Using of non-uniformity mutation brought the obvious mutation at earlier stage and stable mutation in the later stage; this benefited the convergence of units to the best result. In addition, to avoid GA's drawback of converging to local optimization easily, by the limit value of each variable was changed respectively and the genetic operation was performed two times, so the program could work more efficiently and obtained more precise results. Finally, by simulating evolution process of nature biology of a kind self-organize, self-organize, artificial intelligence, this paper established continuous structural optimization model for ten bars cantilever truss, and obtained satisfactory result of optimum design. This paper further explained that structural optimization is practicable with GA's, and provided the theoretic basis for the GA's optimum design of structural engineering.

In this paper, the design problem of local area networks is defined as finding the network topology minimizing cost subject to reliability constraint. The design problem includes issues such as multiple choices of link type for each possible link, multipl

In designing and operating cellular networks, it is assumed that the area of coverage is geographically divided into hexagonal cells. Among these cells, a certain number of cells are chosen to install switches that serve as relays for communications between any pair of cells. Then, each cell is assigned to a switch to complete the cellular network. This decision regarding assignment of cells to switches is known as a CSA (Cell-to-Switch Assignment) problem. Since this problem is so-called NP hard problem, many researches have proposed heuristic-based algorithms to provide near-optimal solutions with a reasonable computation time. Considering these characteristics of the CSA problem, this work develops a genetic algorithm and a local search algorithm. Throu호out a number of experiments, the performance of the proposed algorithms are evaluated, and compared with existing heuristic method

최근 구조최적화분야에서 활발하게 사용되고 있는 유전알고리즘은 해집단을 운용하기 때문에, 많은 반복수와 적응도 평가를 위하여 해집단의 수에 해당하는 구조해석을 필요로 하며, 또한 교배율과 돌연변이율 등의 파라미터에 따라 알고리즘의 성능이 변화하므로 문제에, 따라 적합한 파라미터 설정이 필요한 근본적인 단점을 지니고 있다. 본 연구에서는 기존 유전알고리즘의 단점을 극복할 수 있는 복합유전알고리즘을 마이크로유전알고리즘과 단순유전알고리즘을 결합한 형식으로 그리고, 최적화에 요구되는 연산을 다수의 개인용 컴퓨터에서 동시에 분산하여 수행할 수 있는 고성능 분산 복합유전알고리즘으로 개발하였다. 개발된 알고리즘은 철골 가새골조 구조물의 최소중량설계에 적용하여 그 성능을 평가하였다.

The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plan

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

The objective of this study is the development of a size and shape discrete optimum design algorithms, which is based on the genetic algorithms and the fuzzy theory. This algorithms can perform both size and shape optimum designs of plane and space trusses. The developed fuzzy shape-GAs (FS-GAs) 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. This study solves the problem by introducing the FS-GAs operators into the genetic.

Parallel Genetic Algorithms partition the whole population into several sub-populations and search the optimal solution by exchanging the information each others periodically. Distributed Genetic Algorithm, one of Parallel Genetic Algorithms, divides a large population into several sub-populations and executes the traditional Genetic Algorithm on each sub-population independently. And periodically promising individuals selected from sub-populations are migrated by following the migration interval and migration rate to different sub-populations. In this paper, for the Travelling Salesman Problems, we analyze and compare with Distributed Genetic Algorithms using different Genetic Algorithms and using same Genetic Algorithms on each separated sub-population The simulation result shows that using different Genetic Algorithms obtains better results than using same Genetic Algorithms in Distributed Genetic Algorithms. This results look like the property of rapidly searching the approximated optima and keeping the variety of solution make interaction in different Genetic Algorithms.

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

유전자 알고리즘(GA)은 어떠한 유형의 문제에도 적용가능하며 달리 방법이 없는 경우 최후의 수단으로 흔히 사용되는 방법이다. 강구조물 설계란 기본적으로 구조물을 이루는 부재로서 어떤 재료를 선택될 것인지를 결정하는 문제이다. 따라서 천문학적인 숫자의 설계가 존재하며 이들 중 최적의 설계를 탐색하는 것은 대체로 불가능한 일이다. 본 논문에서는 GA와 이와 관련된 여러 가지 기법들을 소개하고 강구조물 최적설계에 이들의 활용을 모색하였다. 작은 설계공간을 가지는 문제에서는 GA로 전역최적설계를 찾을 수 있었다. GA는 또한 연속변수 최적설계 문제에서도 최적설계를 찾았으며 구조물 최적설계에 적용될 수 있음을 보였다. 그러나 규모가 큰 현실문제에서는 GA가 최적 또는 최적에 근접한 설계를 항상 찾을 수 있을 것이라고 기대하기는 어려울 것으로 생각된다. GA에 G bit improvement를 추가하여 수행한 경우에 더 좋은 최적설계 결과를 보여주었으며 앞으로 이 부분의 연구가 활발해 질 것이다.

다분야 통합 시스템의 설계문제는 다량의 설계변수와 구속조건으로 구성되며 다수의 공학적 현상으로 연관되어 있다. 다분야 통합 최적설계 문제를 효과적으로 다루기 위해서는 다양한 해석분야의 공학적 설계원리를 동시에 고려하여 균형 있고 유기적인 방법으로 최적의 설계를 결정하는 체계적인 설계자동화기술이 요구된다. 다분야 통합 설계문제를 위한 효율적인 설계방법론으로 분리기반 최적화 기법이 적용되는데 이 방법은 한 단위의 대규모 설계문제를 여러 개의 하부시스템으로 분리하여 독립적으로 최적화를 수행하고 각 하부 시스템으로부터의 설계해 사이의 중재 및 통합화를 거쳐 최종적으로 수렴된 최적설계를 찾는 방법이다. 본 논문에서는 분리기반 최적화기법을 다분야 통합최적 설계문제에 적용하는데 필요한 시스템분리기법을 유전알고리즘 및 다층 역전 파 신경회로망을 이용하여 정립하였다. 시스템분리기법을 검증하기 위해 최근 미국 Boeing사에서 개발중인 고속 민간항공기인 HSCT의 시뮬레이션기반 설계문제를 이용하였다. 대규모 설계시스템의 분리결과는 전체 설계문제의 특성을 파악하기 위한 자료로 활용되며 향후, 분리기반 최적화과정에서 최종적으로 통합된 최적설계를 탐색하는데 필요한 기반구조를 제공한다.

Multiple U-typed production lines are increasingly accepted in modern manufacturing system for the flexibility to adjust to changes in demand. This paper considers multiple U line balancing with the objective of minimizing cycle time considering of moving time of workforce given the number of workstation. Like the traditional line balancing problem this problem is NP-hard. In this paper, we show how genetic algorithm can be used to solve multiple U line balancing. For this, an encoding and a decoding method suitable to the problem are presented. Proper genetic operators are also employed. Extensive computational experiments are carried out to show the performance of the proposed algorithm. The computational results show that the algorithm is promising in solution quality.