• 한국해양공학회지
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• 제16권4호
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• pp.25-31
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• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권2호
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• pp.173-180
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• 2012

This paper proposes a novel optimization algorithm inspired by water flowing and shaking behaviors in a vessel. Water drops in our algorithm flow to the gradient descent direction and are sometimes shaken for getting out of local optimum areas when most water drops fall in local optimum areas. These flowing and shaking operations allow our algorithm to quickly approach to the global optimum without staying in local optimum areas. We experimented our algorithm with four function optimization problems and compared its results with those of particle swarm optimization. Experimental results showed that our algorithm is superior to the particle swarm optimization algorithm in terms of the speed and success ratio of finding the global optimum.

• 전자공학회논문지CI
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• 제49권4호
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• pp.54-61
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• 2012

본 논문에서는 기 개발된 등급기준 돌연변이 확률조절방법에 여왕벌진화방법을 융합하여 유전자알고리즘의 성능을 향상시키는 방법을 제안한다. 등급기준 돌연변이 확률조절 방법은 유전자알고리즘의 개체가 지역 최적해에 빠지는 것을 방지하고 지역 최적해에 빠졌을 경우 쉽게 빠져나올 수 있게 하는 방법으로 기존 알고리즘에 비하여 일정부분 성능향상을 보였다. 그러나 이 방법은 지역최적해가 많건 적건 간에 전역 최적해가 한 곳에 작은 영역에 있는 문제에서는 그다지 성능이 좋지 않았다. 우리는 그 이유가 이 방법이 전역 최적해로의 수렴성이 부족한 것으로 판단하고 수렴성을 강화시키기 위하여 여왕벌 진화방법을 융합한 알고리즘을 본 논문에서 제안한다. 여왕벌진화방법은 여왕벌의 생식을 모사한 방법으로 수렴성을 강화시킬 수 있는 방법이다. 제안한 방법의 성능을 측정하기위하여 4개의 함수최적화문제에 적용해본 결과 우리가 예상한대로 전역 최적해가 한 곳에 작은 영역에 몰려있는 문제에서는 상당한 성능향상이 일어나는 것을 관찰할 수 있었다. 그러나 전역 최적해가 넓은 영역에 걸쳐있는 문제에서는 성능향상이 거의 없었으며 전역 최적해가 여러 곳에 멀리 떨어져 있는 문제에서는 강한 수렴성으로 인하여 오히려 성능이 나빠지는 것을 볼 수 있었다. 이러한 실험결과로 보았을 때 본 논문에서 제안한 방법은 전역 최적해가 한 곳에 몰려있는 문제에서 매우 유용하게 사용될 수 있을 것으로 판단된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.101.4-101
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• 2001

Evolutionary algorithm has been receiving a remarkable attention due to the model-free and population-based parallel search attributes and much successful results are coming out. However, there are some problems in most of the evolutionary algorithms. The critical one is that it takes much time or large generations to search the global optimum in case of the objective function with multimodality. Another problem is that it usually cannot search all the local optima because it pays great attention to the search of the global optimum. In addition, if the objective function has several global optima, it may be very difficult to search all the global optima due to the global characteristics of the selection methods. To cope with these problems, at first we propose a preprocessing process, grid-filtering algorithm(GFA), and propose a new distributed evolutionary ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.324-329
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• 2005

Dynamic Encoding Algorithm for Searches (DEAS) which is known as a fast and reliable non-gradient optimization method, was proposed [1]. DEAS reaches local or global optimum with binary strings (or binary matrices for multi-dimensional problem) by iterating the two operations; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., 0 or 1), while UDS performs increment or decrement of binary strings in the BSS' result direction with no change of string length. Because the interval of UDS exponentially decreases with increment of bit string length (BSL), DEAS is difficult to escape from local optimum when DEAS falls into local optimum. Therefore, this paper proposes hopping UDS (HUDS) which performs UDS by hopping as many as BSL in the final point of UDS process. HUDS helps to escape from local optimum and enhances a probability searching global optimization. The excellent performance of HUDS will be validated through the well-known benchmark functions.

• 대한기계학회논문집A
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• 제24권6호
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

• 한국정밀공학회지
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• 제25권9호
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• pp.59-70
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• 2008

Polynomial is used to optimize crown bowl shape of a marine medium speed diesel engine piston. The primary goal of this paper is that it's for an original design through a thermal stress and highest temperature minimum. Piston is modeled using solid element with 6 design variables defined the positional coordinate value. Global optimum of design variables are found and evaluated as developed and integrated with the optimum algorithm combining genetic algorithm(GA) and tabu search(TS). Iteration for optimization is performed based on the result of finite element analysis. After optimization, thermal stress and highest temperature reduced 0.68% and 1.42% more than initial geometry.

• 대한기계학회논문집A
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• 제26권6호
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• pp.1008-1015
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• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• 한국해양공학회지
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• 제17권6호
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• 대한기계학회논문집A
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• 제29권5호
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• pp.754-761
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• 2005

The 40\% of wall thickness criterion which has been used as a plugging rule of steam generator tubes is applicable only to a single cracked tube. In the previous studies performed by authors, several global failure prediction models were introduced to estimate the failure loads of steam generator tubes containing two adjacent parallel axial through-wall cracks. These models were applied for thin plates with two parallel cracks and the COD base model was selected as the optimum one. The objective of this study is to verify the applicability of the proposed optimum global failure prediction model for real steam generator tubes with two parallel axial through-wall cracks. For the sake of this, a series of plastic collapse tests and finite element analyses have been carried out fur the steam generator tubes with two machined parallel axial through-wall cracks. Thereby, it was proven that the proposed optimum failure prediction model can be used as the best one to estimate the failure load quite well. Also, interaction effects between two adjacent cracks were assessed through additional finite element analyses to investigate the effect on the global failure behavior.