• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2005년도 춘계학술대회 논문집
• /
• pp.163-168
• /
• 2005

It is one of the most important things to determinate optimized cutting conditions which satisfy productivity and cost simultaneously in production and CAPP systems. These days many researchers have figured out the optimizing way for solutions of multi-object function to find the approach methods using algorithm such as genetic algorithm or tabu search, etc., instead of mathematical methods. The main creation of objective function is proposed by empirical method but which is difficult to set it up and to analysis. In this paper, an optimization method of cutting condition is shown using the ANN and GA for the multi-objective function in high speed machining.

• 한국공작기계학회논문집
• /
• 제16권4호
• /
• pp.29-35
• /
• 2007

The main objective of this paper is to propose the optimal design method of forging process using genetic algorithm. Design optimization of forging process was doing about one stage and multi stage. The objective function is considered the filling of die. The chosen design variables are die geometry in multi stage and initial billet shape in one stage. We performed FE analysis to simulated forging process. The optimized preform and initial billet shape was obtained by genetic algorithm and FE analysis. To show the efficiency of GA method in forging problem are solved and compared with published results.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
• /
• pp.395-398
• /
• 2002

본 논문에서는 ‘다목적 함수 최적화 문제(Multi-objective Optimization Problem MOP)’를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론 적용시킨 ‘내쉬 유전자 알고리즘(Nash GA)’과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 알고리즘의 결과를 시뮬레이션을 통하여 비교 검토함으로써 ‘진화적 게임 이론(Evolutionary Game Theory : EGT)’의 두 가지 아이디어 -‘내쉬의 균형(Equilibrium)’과 ‘진화적 안정전략(Evolutionary Stable Strategy . ESS)’-에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해를 탐색할 수 있음을 확인한다.

• 산업경영시스템학회지
• /
• 제40권1호
• /
• pp.95-104
• /
• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.187-190
• /
• 2003

Many manufacturing companies consider the integrated and concurrent scheduling because they need the global optimization technology that could manufacture various products more responsive to customer needs. In this paper, we propose an advanced scheduling model to generate the schedules considering resource constraints and precedence constraints in make-to-order (MTO) manufacturing environments. Precedence of work- in-process(WIP) and resources constraints have recently emerged as one of the main constraints in advanced scheduling problems. The advanced scheduling problems is formulated as a multiobjective mathematical model for generating operation schedules which are obeyed resources constraints, alternative workstations of operations and the precedence constraints of WIP in MTO manufacturing. For effectively solving the advanced scheduling problem, the multi-objective hybrid genetic algorithm (m-hGA) is proposed in this paper. The m-hGA is to minimize the makespan, total flow time of order, and maximum tardiness for each order, simultaneously. The m-hGA approach with local search-based mutation through swap mutation is developed to solve the advanced scheduling problem. Numerical example is tested and presented for advanced scheduling problems with various orders to describe the performance of the proposed m-hGA.

• 한국수자원학회논문집
• /
• 제41권1호
• /
• pp.1-15
• /
• 2008

본 연구의 목적은 금강수계의 가장 효율적인 가중치를 찾을 수 있는 절차를 제시하는 것이다. 일반적으로 다목적 최적화 모형의 결과는 목적함수에 부여된 가중치에 크게 좌우되는 경향이 있다. 특히 다목적 저수지 운영 문제의 경우는 어떤 유입량 시나리오가 적용되느냐에 따라 그에 적합한 가중치가 크게 달라질 수 있다. 따라서 유입량의 변동성을 감안해서 저수지 운영자에게 적합한 초기 가중치를 적용하는 것은 매우 큰 의미가 있다. 이에 본 연구는 유입량의 불확실성을 감안하여 적절한 가중치군을 도출할 수 있는 절차를 제안한다. 제안한 절차에서는 다중목적 최적화모형(GA-CoMOM)을 통해 파레토 집합에 대응되는 가중치군을 도출하고, DEA-윈도우분석(DEA-window analysis)과 교차효율성 분석(cross-efficiency analysis)을 사용하여 후보 가중치에 대한 순위를 산정하고, 이 결과를 분석해서 적합한 가중치를 선정한다. 이 절차를 금강 수계 저수지군 연계 운영 문제에 적용한 결과 유입량의 불확실성을 감안해서 가중치를 설정할 수 있었다.

• 대한기계학회논문집B
• /
• 제40권9호
• /
• pp.557-567
• /
• 2016

태양열 가열을 도입한 해양온도차발전용 열교환기(증발기와 응축기)설계 최적화가 수행되었다. 출력은 100kW이고 작동유체는 R134a이며 고성능 상용튜브를 사용하였다. 열전달면적과 압력강하는 관수의 증가와 관통로수의 감소에 따라 서로 상반되는 경향이 존재하므로 이를 해결하기 위하여, 설비투자비에 관련되는 열전달면적과 압력강하에 관련되는 운전비용 최소화를 고려한 두 목적함수를 갖는 유전자 알고리즘(GA)을 이용하여 다목적설계최적화를 수행하였다. 설계최적화 결과, 구현 가능한 최적의 열전달면적 및 압력강하의 조합들이 적정한 관수 및 관통로 수에 대하여 존재하였다. 도출된 증발기와 응축기의 Pareto 선들은 설계자들에게 재정적인 면을 고려하여 선택할 수 있도록 넓은 범위의 최적해를 제공하였다. 또한, 총열전달면적 중 응축기의 열전달면적이 증발기 쪽보다 크게 나타났다.

• Structural Engineering and Mechanics
• /
• 제69권1호
• /
• pp.21-31
• /
• 2019

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

• 조명전기설비학회논문지
• /
• 제17권5호
• /
• pp.118-125
• /
• 2003

본 연구에서는 배전계통에서 부하불평형을 고려한 분산형 전원의 운영에 대한 계획을 제안하였다. 또한, 배전계통의 실제 부하구성 분포를 고려하기 위하여 부하모형은 가정용, 산업용, 상업용, 사무용 및 농업용 부하 등의 집단 부하로 모형화 하였다. 또한, 목적함수로는 계통 유효전력손실을 사용하였고 분산형전원의 수 또는 총용량 및 모선 전압을 제약조건으로 정식화하였으며, 이 목적함수와 제약조건에 대한 부정확한 성질을 평가하기 위하여 퍼지 Goal Programing으로 모델링 하였으며, GA를 사용하여 최적해를 탐색하였다.

• Structural Engineering and Mechanics
• /
• 제11권2호
• /
• pp.151-170
• /
• 2001

With increasing competition, the engineering industry is in need of optimization of designs that would lead to minimum cost or weight. Recent developments in Genetic Algorithms (GAs) makes it possible to model and obtain optimal solutions in structural design that can be put to use in industry. The main objective of this paper is to illustrate typical applications of GAs to practical design of structural systems such as steel trusses, towers, bridges, reinforced concrete frames, bridge decks, shells and layout planning of buildings. Hence, instead of details of GA process, which can be found in the reported literature, attention is focussed on the description of the various applications and the practical aspects that are considered in Genetic Modeling. The paper highlights scope and future directions for wider applications of GA based methodologies for optimal design in practice.