Earticle

Owing to the capability to provide a wide variety of intelligent behaviors, cognitive radio (CR) has become a promising technology to improve spectrum utilization efficiently. One of the popular techniques which adapt CR concept to multi-carrier systems is known as tone injection scheme. This scheme is a sort of peak-to-average-power ratio (PAPR) reduction methods deployable to multi-carrier systems such as orthogonal frequency division multiplexing (OFDM). However, a conventional tone injection scheme might increase averaged transmit power attributed to expanding the size of constellation on purpose to get optimal PAPR reduction. Based on a weighted-sum genetic algorithm to resolve multi-objective optimization problem (MOOP), the modified tone injection scheme exploits the agility of CR technology to rapidly adapt operating parameters in order to fulfill PAPR reduction as well as mitigation of power increase optimally. The simulation results verify that the proposed scheme is flexible because it could not only control the performance of PAPR reduction, but also alleviate power increase by steering weight values at the expense of relatively low complexity comparing with other conventional method.

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

This paper presents a method that quickly search for sweet spot of array antenna, and keep it for fast speed transmission in millimeter wave. The proposed method is the Modified Genetic Algorithm (MGA) and Fuzzy Logic system. The MGA is used to find the approximate range as a pre-processing function. And the fuzzy logic system is used to find the exact sweet spot. We can transfer and receive a large data in millimeter wave bandwidth. Because antenna signal accounts for a few bits, influence on communication performance is little. MGA operators are used one-point crossover, rank-based selection, bit string mutation, elitism and 16bit split gene. In the fuzzy logic system, Rule base inference is used the max-min inference procedure. Defuzzification is used the center of gravity computation. The efficiency of the proposed method is verified by means of simulations.

The objective of this paper is to Develop and Test a model of optimizing the early design phase of shell and Tube Heat Exchangers via the application of modified Genetic Algorithm (MGA).The Modified Genetic Algorithm is based on the integration of classical genetic algorithm structure and a systematic neighborhood structure .The MGA model can help the designers to make decisions at the early phases of the design process. With a MGA model, it is possible to obtain an approximately better prediction, even when required information is not available in the design process. This model proved that MGA is capable of providing better solutions with higher quality even with inadequate data.

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Neural Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

Genetic algorithm is one of the best ways to solve a discrete variable optimization problem. Genetic algorithm tends to thrive in an environment in which the search space is uneven and has many hills and valleys. In this study, genetic algorithm is used for solving the design problem of gable structure. The design problem of frame structure has some special features(complicate design space, many nonlinear constrants, integer design variables, termination conditions, special information for frame members, etc.), and these features must be considered in the formulation of optimization problem and the application of genetic algorithm. So, 'FRAME operator', a new genetic operator for solving the frame optimization problem effectively, is developed and applied to the design problem of gable structure. This example shows that the new opreator has the possibility to be an effective frame design operator and genetic algorithm is suitable for the frame optimization problem.

This paper presents a novel scheme that quickly searches for the sweet spot of multiple array antennas, and locks on to it for high-speed millimeter wavelength transmissions. The proposed method utilizes a modified genetic algorithm, which selects a superior initial group through preprocessing in order to solve the local solution in a genetic algorithm. TDD (Time Division Duplex) is utilized as the transfer method and data controller for the antenna. Once the initial communication is completed for the specific number of individuals, no longer antenna's data will be transmitted until each station processes GA in order to produce the next generation. After reproduction, individuals of the next generation become the data, and communication between each station is made again. Simulation results confirmed the efficiency of the proposed method.

건물의 경우, 용도 변경에 따른 중력하중 변화, 시공 단계에 따라 중력하중 변화 등이 구조물 시스템에 영향을 미친다. 따라서, 본 연구에서는 시스템 식별 변수 설정에 있어 기존에 강성만을 변수로 설정한 방법에 추가적으로 질량을 변수로 설정하여 시스템을 식별하는 기법을 제안한다. 계측한 동특성과 FE모델에서 추출한 동특성 간의 차이를 최소화하여 변수를 탐색하게 된다. 최소화 기법으로 변형 유전 알고리즘을 적용하였다. 보다 전역적 해탐색을 위해 변형 유전 알고리즘은 더 넓은 해 탐색 공간에서 해를 찾는다. 철골 보 구조물의 시뮬레이션을 통해 본 연구가 제시한 기법을 검증하였고 변형 유전 알고리즘과 기존의 단순 유전 알고리즘의 성능을 비교하였다. 또한, 강성 식별만을 수행한 기존 연구의 방법과 본 연구가 제시한 기법간의 차이를 비교하였다.

In the buildings, the systems of structures are influenced by the gravity load changes due to room alteration or construction stage. This paper proposes a system identification method establishing mass as well as stiffness to parameters in model updating process considering mass change in the buildings. In this proposed method, modified genetic algorithm, which is optimization technique, is applied to search those parameters while minimizing the difference of dynamic characteristics between measurement and FE model. To search more global solution, the proposed modified genetic algorithm searches in the wider search space. It is verified that the proposed method identifies the system of structure appropriately through the analytical study on a steel beam structure in the building. The comparison for performance of modified genetic algorithm and existing simple genetic algorithm is carried out. Furthermore, the existing model updating method neglecting mass change is performed to compare with the proposed method.

본 논문에서는 부모 개체의 해밍 거리에 기반하여 선택적 변이연산을 적용한 유전알고리즘을 제안한다. 유전자 형이 매우 유사한 개체들 간의 유전연산은 알고리즘의 탐색성능을 저하시키고 조기 수렴의 가능성을 증가시킨다. 본 논문에서는 이러한 현상을 극복하기 위하여, 교차연산 시 선택된 두 부모 개체간의 해밍 거리에 따라 그 값이 낮으면 교차연산 후 생성된 두 자식 개체 중 한쪽에게 높은 변이확률을 적용하고 다른 한쪽 자식은 부모와 비슷한 유전자 형으로 탐색을 계속하게 하여 조기 수렴을 방지하면서 해집단의 다양성 유지 기능을 향상 시켰다. 제안한 유전 알고리즘을 다차원 배낭 문제에 적용한 결과, 같은 조건에서 단순 유전 알고리즘(SGA) 보다 향상된 탐색 성능을 보여주었다.

본 논문에서는 부모 개체의 해밍 거리에 기반하여 선택적 변이연산을 적용한 유전알고리즘을 제안한다. 유전자 형이 매우 유사한 개체들 간의 유전연산은 알고리즘의 탐색성능을 저하시키고 조기 수렴의 가능성을 증가시킨다. 본 논문에서는 이러한 현상을 극복하기 위하여, 교차연산 시 선택된 두 부모 개체간의 해밍 거리에 따라 그 값이 낮으면 교차연산 후 생성된 두 자식 개체 중 한쪽에게 높은 변이확률을 적용하고 다른 한쪽 자식은 부모와 비슷한 유전자 형으로 탐색을 계속하게 하여 조기 수렴을 방지하면서 해집단의 다양성 유지 기능을 향상 시켰다. 제안한 유전 알고리즘을 다차원 배낭 문제에 적용한 결과, 같은 조건에서 단순 유전 알고리즘(SGA) 보다 향상된 탐색 성능을 보여주었다.

외판원(Traveling Salesman Problem)는 계산 복잡도가 매우 높으므로 이를 해결하려는 다양한 방법들이 제시되어 왔다. 최근에는 특히 휴리스틱(Heuristic) 에 기반한 유전 알고리즘(Genetic Algorithm)에 위한 방법이 관심을 집중시키고 있고, 이를 위한 다양한 교잡(Crossiver)연산자와 돌연변이(Mutation) 연산자들이 발표되고 있다. 돌연변이연산자는 지역해에 빠지는 것을 방지하며, 유용한 유전 특성을 잃어버릴 위험이 있는 교잡 연산자의 단점을 보완할 수 있다. 본 논문에서는 새로운 돌연변이 연산자를 개발하여 적용한 유전 알고리즘으로 외판원 문제를 해결한다.

본 논문에서는 유전자 알고리즘을 이용한 rural Postman problem with Time windows(RPPTW) 해법을 위해 유전자 알고리즘에 사용되는 교차 연산자를 제안하고, 기존의 교차 연산자와 비교한다. RPPTW는 다중목적 최적화 문제로서, Rural Postman Problem(RPP)에 서비스 시간 제한을 위한 시간 윈도우(Time Windows)를 두고 제한된 시간 내에 서비스를 받을 수 있도록 구성된 문제이다. 따라서, RPPTW는 주어진 시간 내에 서비스를 받으면서 최소 비용으로 라우팅을 하는 다중 목적 최적화 문제이다. 다중 목적 최적화 문제인 RPPTW를 해결하기 위해서는 Pareto-optimal 집합을 구해야 한다. Pareto-optimal 집합은 각 목적값들의 우수성을 비교할 수 없는 집합이다. 본 논문에서는 12개의 임의로 생성된 문제들에 대해 3개의 교차 연산자를 사용하여 실험을 하여 그 결과를 비교하였다. 본 논문에서 사용된 교차 연산자들은 PMX(Partially Matched Exchange), OX(Order Exchange), 그리고 본 논문에서 제안한 MOX(Modified Order Exchange)이다. 각 문제들에 대한 실험 결과를 통해서 RPPTW를 위한 교차 연산자 중에 본 논문에서 제안한 MOX방법이 효율적임를 알 수 있었다.

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.