Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

Direct spring loaded pressure relief valve(DSLPRV) is a safety valve to relax surge pressure of the pipeline system. DSLPRV is one of widely used safety valves for its simplicity and efficiency. However, instability of the DSLPRV can caused by various reasons such as insufficient valve volume, natural vibration of the spring, etc. In order to improve reliability of DSLPRV, proper selection of design factors of DSLPRV is important. In this study, methodology for selecting design factors for DSLPRV was proposed. Dynamics of the DSLPRV disk was integrated into conventional 1D surge pressure analysis. Multi-objective genetic algorithm was also used to search optimum design factors for DSLPRV.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

Recently, a concept of damped outrigger system has been proposed for tall buildings. Structural characteristics and design method of this system were not sufficiently investigated to date. In this study, control performance of damped outrigger system for building structures subjected to seismic excitations has been investigated. And optimal design method of damped outrigger system has been proposed using multi-objective genetic algorithm. To this end, a simplified numerical model of damped outrigger system has been developed. State-space equation formulation proposed in previous research was used to make a numerical model. Multi-objective genetic algorithms has been employed for optimal design of the stiffness and damping parameters of the outrigger damper. Based on numerical analyses, it has been shown that the damped outrigger system control dynamic responses of the tall buildings subjected to earthquake excitations in comparison with a traditional outrigger system.

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.

인접 구조물의 지진응답 제어를 위한 비선형 감쇠시스템의 최적 설계 방법에 관하여 연구하였다. 최적 설계를 위한 목적 함수로는 구조물의 응답과 감쇠기의 총 사용량을 고려하였으며, 상충하는 두 목적함수를 합리적인 수준에서 동시에 최소화하는 해를 구하기 위하여 유전자 알고리즘에 기반한 다목적 최적화 방법을 도입하였다. 또한, 최적화 과정에서 요구되는 비선형 시간이력해석을 수행하지 않고도, 비선형 이력감쇠기로 연결된 구조물의 지진응답을 효율적으로 평가하기 위하여 추계학적 선형화 방법을 접목하였다. 제시하는 방법의 효율성을 검증하기 위한 수치 예로서 20층과 10층의 인접 빌딩을 고려하였으며, 두 빌딩을 연결하는 비선형 감쇠시스템으로는 입력전압의 크기에 따라 변화하는 감쇠성능을 보이는 MR 감쇠기를 도입하였다. 제시하는 방법을 통하여 MR 감쇠기의 각 층별 최적 개수 및 감쇠용량을 결정할 수 있었으며, 이는 일반적인 균등분포 시스템에 비해 유사한 제어성능을 보이면서도 훨씬 경제적이었다. 또한, 인접구조물간 충돌에 대하여도 확률적으로 안정적인 거동을 보임을 검증하였으며, 제시하는 방법이 준능동 제어시스템의 최적 배치를 결정하기 위한 설계문제에도 적용할 수 있음을 보였다.

In this study, an adaptive shared control system for adjacent tall building structures subjected to seismic loads has been investigated using multi-objective genetic algorithms. A tuned mass damper (TMD) was shared with an adjacent building structure in this study. Variable damping or stiffness devices were used to make a controllable shared TMD. Control objectives of the adjacent tall buildings connected by a adaptive shared TMD can be conflict. This kind of problem can be solved using multi-objective optimization techniques that provide a suite of Pareto-optimal solutions. A possibility of application of multi-objective genetic algorithms to design of a adaptive shared TMD for vibration control of adjacent tall buildings has been investigated.

본 연구의 목적은 다목적 유전자 알고리즘을 이용하여 우수유출 저류지를 소유역에 분담하여 설치 계획하는데있다. 이를 위해 우수유출 저류지의 위치 및 규모를 최적화하기 위한 모형을 개발하였다. 이 모형은 크게 2가지로 나뉘어 지는데, 유역유출모형과 최적해를 구하기 위하여 도입한 다목적 유전자 알고리즘(MOGAs)이다. 이러한 최적화 모형을 모의하기 위하여 목적함수는 첨두유출량과 저류지 저류용량의 함수로 설정하고, 제한조건은 기본적으로 구조적 제한과 저류용량

본 연구는 다목적 유전자알고리즘을 이용하여 Tank 모형의 매개변수를 추정하는데 있어서 선호적순서화(preference ordering)를 적용한 연구로써, 목적함수의 개수가 여러 개인 경우에 발생할 수 있는 파레토최적화의 단점을 해결하기 위한 것이다. 최적화를 위한 목적함수는 모두 4가지를 사용하였으며, 선호적순서화를 통해서 구한 2차 효율성(2nd order efficiency)을 가지면서 정도(degree)가 3인 4개의 해 중에서 1개의 해만을

본 연구의 목적은 개념적인 강우-유출모형인 Tank 모형의 매개변수를 산정하기 위한 다목적 유전자알고리즘의 적용성을 평가하는 것이다. 다목적 유전자알고리즘 기법으로는 최근에 가장 많이 사용되는 기법중의 하나인 NSGA-II를 채택하여 Tank 모형과 결합하였으며, 4가지 목적함수(유출용적오차, 평균제곱근 오차, 고수유량 평균제곱근 오차 및 저수유량 평균제곱근 오차)값을 최소화하는 형태의 목적함수를 적용하였다. NSGA-II는 목적함수의 개수가 많아지면