• Water for future
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• v.28 no.2
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• pp.169-180
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• 1995

The cost of a looped pipe network is affected by a set of loop flows. The mathematical model for optimizing the looped pipe network is expressed in the optimal set of loop flows to apply to a stochastic optimization method. Because the feasible region of the looped pipe network problem is nonconvex with multiple local optima, the Modified Stochastic Probing Method is suggested to efficiently search the feasible region. The method consists of two phase: i) a global search phase(the stochastic probing method) and ii) a local search phase(the nearest neighbor method). While the global search sequentially improves a local minimum, the local search escapes out of a local minimum trapped in the global search phase and also refines a final solution. In order to test the method, a standard test problem from the literature is considered for the optimal design of the paralled expansion of an existing network. The optimal solutions thus found have significantly smaller costs than the ones reported previously by other researchers.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1023-1030
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• 2007

This paper presents a new and efficient approach for solving the economic dispatch (ED) problems with nonconvex cost functions using particle swarm optimization (PSO). Although the PSO is easy to implement and has been empirically shown to perform well on many optimization problems, it may easily get trapped in a local optimum when solving problems with multiple local optima and heavily constrained. This paper proposes an improved PSO, which combines the conventional PSO with chaotic sequences (CPSO). The chaotic sequences combined with the linearly decreasing inertia weights in PSO are devised to improve the global searching capability and escaping from local minimum. To verify the feasibility of the proposed method, numerical studies have been performed for two different nonconvex ED test systems and its results are compared with those of previous works. The proposed CPSO algorithm outperforms other state-of-the-art algorithms in solving ED problems, which consider valve-point and multi-fuels with valve-point effects.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.798-806
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• 2003

Structural optimization has been carried out in continuous or discrete design space. Methods for continuous design have been well developed though they are finding the local optima. On the contrary, the existing methods for discrete design are extremely expensive in computational cost or not robust. In this research, an algorithm using tabu search is developed fur the discrete structural designs. The tabu list and the neighbor function of the Tabu concepts are introduced to the algorithm. It defines the number of steps, the maximum number for random searches and the stop criteria. A tabu search is known as the heuristic approach while genetic algorithm and simulated annealing algorithm are attributed to the stochastic approach. It is shown that an algorithm using the tabu search with random moves has an advantage of discrete design. Furthermore, the suggested method finds the reliable optimum for the discrete design problems. The existing tabu search methods are reviewed. Subsequently, the suggested method is explained. The mathematical problems and structural design problems are investigated to show the validity of the proposed method. The results of the structural designs are compared with those from a genetic algorithm and an orthogonal array design.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.747-752
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• 2011

HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.944-951
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• 2015

Pulse Width Modulation (PWM) techniques are increasingly employed for PWM inverter fed induction motor drive. Among various popular PWM methods used, Selective Harmonic Elimination PWM (SHEPWM) has been widely accepted for its better harmonic elimination capability. In addition, using SHEPWM, it is also possible to maintain better voltage regulation. Hence, in this paper, an attempt has been made to apply Bacterial Foraging Algorithm (BFA) for solving selective harmonic elimination problem. The problem of voltage harmonic elimination together with output voltage regulation is drafted as an optimization task and the solution is sought through proposed method. For performance comparison of BFA, the results obtained are compared with other techniques such as derivative based Newton-Raphson method, and Genetic Algorithm. From the comparison, it can be observed that BFA based approach yields better results. Further, it provides superior convergence, reduced computational burden, and guaranteed global optima. The simulation results are validated through experimental findings.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1322-1327
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• 2003

It is important to determine supporting locations for structural stability of a structure under non-uniform load in space interfered by other parts. In this case, There are many local optima with discontinuous design space. Therefore, The traditional optimization methods based on derivative are not suitable. Whereas, Genetic algorithm(GA) based on stochastic search technique is a very robust and general method. This paper has been presented to determine supporting locations of the vertical supports for reducing stress of the KSTAR(Korea super Superconducting Tokamak Advanced Research) IVCC(In-vessel control coil) under non-uniform electromagnetic load and space interfered by other parts using genetic algorithm. For this study, we develop a program combining finite element analysis with a genetic algorithm to perform structural analysis of IVCC. In addition, this paper presents a technique to perform optimization with FEM when design variables are trapped in an incongruent design space.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.1
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• pp.20-27
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• 2002

Aircraft design requires the intergration of several disciplines, inculding aerodynamics, structures, controls. To achieves advances in performance, each technology, or discipline must be more accurate in analysis and must be more highly intergrated. One of the important interdisciplinary interactions in mordern aircraft design is that of aerodynamics and structures. In this study, for increasing accuracy in each discipline's analysis, CFD for aerodynamic analysis and FEM for structurral analysis was used and, for considering important interdisciplinary interactions, aeroelastic effect was considered. As optimization algorithm, PBIL algorithm was used for global optima and was parallelized to alleviate the computational burden. The efficiency and accuracy of the present method was assesed by range maximiziation of reference of reference wing.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.729-732
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• 2010

본 논문에서는 이산 변수 최적화에 적합한 유전 알고리듬을 이용하여 복합재 적층 패치의 최적강도설계를 수행하였다. 기저판(substrate)와 접착제(adhesive), 그리고 복합재 적층 패치로 이루어진 구조물에서 패치의 강도를 효율적으로 구하기 위해서 응력 함수 기반의 해석적 방법을 도입하였다. 면외 방향의 응력 함수를 가정하여 가상 공액일의 법칙(complementary virtual work principle)에 적용하였으며, 복합재 패치의 자유 경계조건으로부터 면내 방향의 응력함수를 결정하였다. 응력 함수를 통하여 구한 층간 응력 값은 자유 경계 효과를 잘 나타내었고, 이를 이용하여 패치의 강도 해석을 수행하였다. 강도 해석 시, 복합재 패치의 파괴 기준은 면내 응력들에 대해서는 최대 응력 척도를 사용하였으며, 층간 응력들에 대해서는 quadratic delamination 척도를 사용하였다. 유전 알고리듬을 이용한 최적강도설계 과정에서는 임의의 염색체가 주어진 적층 구속 조건을 만족할 수 있게 수정(repairing)하는 과정을 도입하였다. 또한 다수의 전역해(global optima)를 효과적으로 찾기 위해서 multiple elitism 기법을 도입하였다. 응력 함수 기반의 강도 해석방법과 유전 알고리듬과의 연계를 통한 복합재 적층 패치의 강도최적설계 기법은 패치 구조물의 해석 및 설계에 있어서 효율적인 도구로서 사용할 수 있을 것이라 사료된다.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.1
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• pp.93-101
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• 1997

Generally the traditional optimization methods have possibilities not only to give a different optimum value according to their starting point, but also to get to local optima. On the other hand, Genetic Algorithm (GA) has an ability of robust global search. In this paper, a new optimization method - the combination of traditional optimization method and genetic algorithm - is presented so as to overcome the above disadvantage of traditional methods. In order to increase the efficiency of the hybrid optimization method, a knowledge-based reasoning is adopted in the part of mathematical modeling, algorithm selection, and process control. The validation of the developed knowledge-based hybrid optimization method was examined and verified applying the method to nonlinear mathematical models.