영구자석 선형 전동기인 VCM(Voice coil motor)은 직접 구동 방식의 액츄에이터로 기어나 변속장치가 필요 없어 높은 정밀도 를 가지고 구조적인 특성상 기계적 마찰이 적어 소음이 발생하지 않는 장점을 가지고 있다. 아울러 회전운동을 직선 운동으로 변환하기 위한 별도의 장치가 필요하지 않고, 구동부가 가벼워 응답속도가 빠른 특징이 있다. 본 연구에서는 이러한 VCM을 다양한 산업 분야에 적용하기 위한 기초연구로 VCM의 속도제어를 위해 PSO(Pariticle swarm optimization) 기법을 적용하여 제어기의 유용성 평가를 위한 수 치 시뮬레이션을 수행하였다. 제어계는 전류와 속도 제어를 위한 이중 루프로 구성하였고, 각각의 제어 루프에는 PI 제어기를 적용하여 속도 목표치에 추종하는 출력값을 얻기 위한 제어기를 설계하였다. 제어기 파라미터 추정에는 PSO기법을 적용하였고, 제어기의 유용성 을 검증하기 위해 주파수 영역에서의 모델매칭기법을 적용한 제어 기법과의 제어 결과를 비교하였다. 두 가지 제어 기법은 MATLAB을 이용하여 수치 시뮬레이션을 수행했고, 제어 결과는 IAEU(Integral of absolute error units) 평가 지수를 이용하여 비교하였다. 수치 시뮬레 이션 결과 제안한 제어 기법의 유용성을 확인할 수 있었다.

페어리드 체인 스토퍼(Fairlead Chain Stopper, FCS)는 10MW급 부유식 해상풍력 발전기에 설치하기 위해 새롭게 개발된 탈착형 계류 시스템이다. 본 연구에서는 다양한 메타모델과 입자군집최적화 알고리즘을 이용하여 FCS의 구조설계에 대한 최적설계안을 탐색하 였다. FCS의 구조설계는 선급규정 설계하중조건을 산정하여 유한요소해석을 통해 수치해석적으로 평가하였고, 수치해석모델을 최적설계 에 연계하여 적용하였다. 최적설계의 수렴 효율성을 향상시키기 위해 반응표면모델, 크리깅, 체비쇼프직교다항식, 그리고 신경망과 같은 다양한 메타모델이 사용되었다. 최적설계에서 제한조건은 설계하중조건 별 응력을 고려하였고, 주요 구조부품의 두께 치수를 이산설계변 수와 연속설계변수로 각각 적용하여 목적함수인 최소중량설계를 달성할 수 있는 최적해의 특성을 비교하였다. 최적설계 알고리즘은 이산 설계변수의 최적해 탐색이 가능한 입자군집최적화가 적용되었다. FCS의 구조설계에 대해 신경망 기반의 메타모델이 적용된 경우에 4.72% 이하의 오차율로 최적해의 결정이 가능한 것으로 확인되었다.

Due to cognitive differences, traditional perceptual engineering (KE) frequently relies too heavily on designers' experience in analyzing customers' emotional demands, which can result in product designs that deviate from users' expectations. This work suggests a thorough evaluation approach that combines the particle swarm optimization-support vector regression (PSO-SVR) model and perceptual engineering to increase the scientificity and precision of design choices. The approach first determines the subjective weights of users' emotional needs using spherical fuzzy hierarchical analysis (SFAHP). Next, it uses the entropy weighting method to determine the objective weights. Finally, it combines the subjective and objective data using game theory to produce a more rational evaluation system. Finally, the emotional prediction model based on PSO-SVR is constructed to realize the accurate mapping between emotional needs and design features. The empirical study shows that“speed”, “dynamic”and“luxury” are the core emotional demands of users, and the algorithm's prediction results are highly consistent with users' actual evaluations, which strongly verifies the accuracy of the model. Compared with the traditional KE method, the model better integrates subjective experience and objective data and provides more practical support for the design of flybridge yachts.

PURPOSES : A model for minimizing cutting loss and determining the optimum layout of blocks in pavements was developed in this study. METHODS : Based on literature review, a model which included constraints such as the amount, volume, overlap, and pattern, was developed to minimize the cutting loss in an irregular pavement shape. The Stach bond, stretcher bond, and herringbone patterns were used in this model. The harmony search and particle swarm algorithms were then used to solve this model. RESULTS : Based on the results of the model and algorithms, the harmony search algorithm yielded better results because of its fast computation time. Moreover, compared to the sample pavement area, it reduced the cutting loss by 20.91%. CONCLUSIONS : The model and algorithms successfully optimized the layout of the pavement and they have potential applications in industries, such as tiling, panels, and textiles.

Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.

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

We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.

Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such a

멀티모달 최적화알고리듬의 일종인 ISPSO와 불확실도분석기법인 GLUE를 결합한 ISPSO-GLUE 기법을 TOPMODEL의 불확실도분석에 적용하였으며, 그 결과를 GLUE 기법과 비교하였다. 두 기법 모두 같은 횟수만큼 모형을 실행하였을 때 ISPSO-GLUE 기법의 누적성능이 더 좋아지는 시점을 발견할 수 있었으며, 그 이후로도 ISPSO-GLUE 기법은 GLUE 기법과는 달리 점진적인 성능의 향상을 보여 주었다. 두 기법이 비슷한 모양과 양상의 95% 불확실도구간을 생성하였다. 하지만 ISPSO-GLUE 기법이 약 5.4배 더 많은 관측치를 포함하는 것으로 나타났으며 GLUE 기법에 비해 훨씬 적은 횟수의 모형실행으로도 좋은 성능의 불확실도구간을 얻을 수 있는 것으로 나타났다. ISPSO-GLUE 기법과 비교했을 때 GLUE 기법이 최대 첨두유량의 감쇠곡선 부분에서 불확실도를 과대평가하였다. 이 시간대에 대해서는 GLUE의 경우 불확실도를 줄이기 위해 더 많은 행동모형들을 찾을 필요가 있다. ISPSO-GLUE 기법이 정량적인 성능평가에서 훨씬 많은 관측치를 포함할 수 있었다는 것은 이 기법의 가능성을 잘 보여 주었다고 할 수 있으며, 특히 계산적으로 값비싼 수문모형에서는 보다 큰 성능의 차이를 보일 것으로 기대된다.