• Journal of Ocean Engineering and Technology
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• v.16 no.4
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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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• v.12 no.2
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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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.54-61
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• 2012

This paper proposes a fusion method of the queen-bee evolution into the rank-based control of mutation probability for improving the performances of genetic algorithms. The rank-based control of mutation probability which showed some performance improvements than the original method was a method that prevented individuals of genetic algorithms from falling into local optimum areas and also made it possible for the individuals to get out of the local optimum areas if they fell into there. This method, however, showed not good performances at the optimization problems that had a global optimum located in a small area regardless of the number of local optimum areas. We think that this is because the method is insufficient in the convergence into the global optimum, so propose a fusion method of the queen-bee evolution into this method in this paper. The queen-bee evolution inspired by reproduction process of queen-bee is a method that can strengthen the convergency of genetic algorithms. From the extensive experiments with four function optimization problems in order to measure the performances of proposed method we could find that the performances of proposed method was considerably good at the optimization problems whose global optimum is located in a small area as we expected. Our method, however, showed not good performances at the problems whose global optima were distributed in broad ranges and even showed bad performances at the problems whose global optima were located far away. These results indicate that our method can be effectively used at the problems whose global optimum is located in a small area.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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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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• 2005.06a
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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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.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.9
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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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.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.5 s.236
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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.