• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.55-56
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• 2015

근래에 게임이론 분야에서 진화계산 기법을 사용한 분석은 중요한 이슈이다. 본 논문에서는 교섭게임에서 입자군집최적화와 차분진화알고리즘 간의 공진화 과정을 관찰하고 상호 경쟁에서 얻는 이득을 비교하여 두 알고리즘의 성능을 분석한다. 실험결과 입자군집최적화가 차분진화알고리즘에 비해 교섭게임에서 더 우수한 성능을 보임을 확인하였다.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.549-557
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• 2014

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

• Journal of KIISE
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• v.41 no.12
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• pp.1090-1098
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• 2014

In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.636-643
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• 2020

The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.323-327
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• 2010

본 논문은 차분 진화 알고리즘의 최적해 탐색 능력을 향상시키기 위해 병렬 처리기법을 적용한 기법을 제안한다. 이를 위해서 기존의 개체군들을 5개의 그룹으로 나누어서 독립적으로 최적화 과정을 하도록 하여 일정한 확률에 의해서 각 그룹이 다른 그룹의 Best individual들을 변이 과정에서 참조하도록 하였다. 이러한 방식을 통해서 기존 차분 진화 알고리즘이 가지고 있는 지역해 수렴 문제를 해결하는 할 수 있도록 하였다. 실험을 통해서 제안된 차분 진화 알고리즘(P-DE)의 탐색 능력을 비교 및 분석 하였다. 실험 결과 제안된 차분 진화 알고리즘(P-DE)이 지역해 수렴 문제를 충분히 해결함으로써 기존의 알고리즘에 비해서 우수한 성능을 보이는 것을 확인 하였다.

• Korean Journal of Optics and Photonics
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• v.27 no.6
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• pp.203-211
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• 2016

We have studied the hybrid method for reconstructing apodized chirped fiber Bragg gratings, using both an analytic estimation of grating parameters and an optimization algorithm. The Hilbert transform of the reflection spectrum was utilized to estimate grating parameters, and then the layer-peeling algorithm was used to obtain refined parameter values by the differential-evolution optimization process. Calculations for a fiber Bragg grating with wavelength chirp rate 2 nm/cm were obtained with an accuracy of $6{\times}10^{-5}nm/cm$ for the chirp rate and $3{\times}10^{-9}nm/cm$ for the index modulation, with much improved calculation speed and high reliability.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1809-1814
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• 2003

Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.2
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• pp.183-193
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• 2016

Estimation of the stability of an operating tunnel through a back analysis is a difficult concept to analyze. Specially, when a relatively thick lining is constructed as in case of a subsea tunnel, there will be a limit to the use of displacement-based tunnel back analysis because the corresponding displacement is too small. In this study, DEA is adopted for tunnel back analysis and the feasibility of DEA for back analysis is evaluated. It is implemented in the finite difference code FLAC3D using its built-in FISH language. In addition, the stability of a tunnel lining will be evaluated from the development of displacement-based algorithm and its expanded algorithm with conformity of several parameters such as stress measurements.

• Journal of KIISE
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• v.43 no.2
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• pp.204-211
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• 2016

In this paper, we propose an automatic melody composition system that can generate a sophisticated melody by adding non-harmony tone in the given chord progression. An overall procedure consists of two steps, which are the rhythm generation and melody generation parts. In the rhythm generation part, we designed new fitness functions for rhythm that can be controlled by a user setting parameters. In the melody generation part, we designed new fitness functions for melody based on harmony theory. We also designed evolutionary operators that are conducted by considering a musical context to improve computational efficiency. In the experiments, we compared four metaheuristics to optimize the rhythm fitness functions: Simple Genetic Algorithm (SGA), Elitism Genetic Algorithm (EGA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, we compared proposed genetic algorithm for melody with the four algorithms for verifying performance. In addition, composition results are introduced and analyzed with respect to musical correctness.