• Journal of the Optical Society of Korea
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• v.17 no.3
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• pp.237-241
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• 2013

This paper presents an efficient evolutionary method to optimize the gain ripple of multi-pumps photonic crystal fiber Raman amplifier using the Fuzzy Adaptive Modified PSO (FAMPSO) algorithm. The original PSO has difficulties in premature convergence, performance and the diversity loss in optimization as well as appropriate tuning of its parameters. The feasibility and effectiveness of the proposed hybrid algorithm is demonstrated and results are compared with the PSO algorithm. It is shown that FAMPSO has a high quality solution, superior convergence characteristics and shorter computation time.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.844-846
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• 1998

In this study, an adaptive evolutionary computation(AEC), which uses adaptively a genetic algorithm having global searching capability and an evolution strategy having local searching capability with different methodologies, is suggested. This paper develops AEC for solving ELD problem with piecewise quadratic cost function. Numerical results show that the proposed AEC can provide accurate dispatch solutions within reasonable time for the ELD problem with piecewise quadratic cost function.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1118-1120
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• 1998

In this study, an adaptive evolutionary computation (AEC), which uses adaptively a genetic algorithm having global searching capability and an evolution strategy having local searching capability with different methodologies, is suggested. We applied the AEC to design of fuzzy logic controllers for a PSS (power system stabilizer). FLCs for PSS controllers are designed for damping the low frequency oscillations caused by disturbances such as tile sudden changes of loads, outages in generators, transmission line faults, etc. The membership functions of FLCs is optimally determined by AEC.

• Journal of KIISE:Information Networking
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• v.37 no.3
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• pp.228-233
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• 2010

During wireless network operations, adaptive power control is an effective way to enhance the network performance. In this paper, a new online power control scheme is proposed based on the evolutionary game theory. To converge a desirable network equilibrium, the proposed scheme adaptively adjusts a transmit power level in a distributed online manner. With a simulation study, we demonstrate that the proposed scheme improves network performance under widely diverse network environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.694-705
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• 2010

The mutation operation is the main operation in the evolutionary programming which has been widely used for the optimization of real valued function. In general, the mutation operation utilizes both a probability distribution and its parameter to change values of variables, and the parameter itself is subject to its own mutation operation which requires other parameters. However, since the optimal values of the parameters entirely depend on a given problem, it is rather hard to find an optimal combination of values of parameters when there are many parameters in a problem. To solve this shortcoming at least partly, if not entirely, in this paper, we propose a new mutation operation in which the parameter for the variable mutation is theoretically estimated from the self-adaptive perspective. Since the proposed algorithm estimates the scale parameter of the Cauchy probability distribution for the mutation operation, it has an advantage in that it does not require another mutation operation for the scale parameter. The proposed algorithm was tested against the benchmarking problems. It turned out that, although the relative superiority of the proposed algorithm from the optimal value perspective depended on benchmarking problems, the proposed algorithm outperformed for all benchmarking problems from the perspective of the computational time.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.51-57
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• 2014

In this paper, we introduce the adaptive threshold detection scheme based on an evolutionary arithmetic algorithm for the M-ary holographic data storage(HDS) system. The genetic algorithm is a particular class of evolutionary arithmetic based on the process of biological evolution, which is a very promising technique for optimization problem and estimation applications. In this study, to improve the detection performance that is degraded by the HDS channel environment and the pixel misalignment, the threshold value was assumed to be a population set of the evolutionary algorithm. The proposed method can find an appropriate population set of bit threshold, which minimizes bit error rate(BER) as increased generation. For performance evaluation, we consider severe misalignment effect in the 4-ary holographic data storage system. Furthermore, we measure the BER performance and compare the proposed methods with the conventional threshold detection scheme, which verifies the superiority of the proposed scheme.

• Journal of the Korean Institute of Navigation
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• v.24 no.5
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• pp.361-371
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• 2000

This paper treats a genetic algorithm for ship scheduling problem in set packing formulation. We newly devised a partition based representation of solution and compose initial population using a domain knowledge of problem which results in saving calculation cost. We established replacement strategy which makes each individual not to degenerate during evolutionary process and applied adaptive mutate operator to improve feasibility of individual. If offspring is feasible then an improve operator is applied to increase objective value without loss of feasibility. A computational experiment was carried out with real data and showed a useful result for a large size real world problem.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.59-66
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• 2004

In this paper, a fast 3D model extraction algorithm with an enhanced PBIL of preserving depth consistency is proposed for the extraction of 3D depth information from 2D images. Evolutionary computation algorithms are efficient search methods based on natural selection and population genetics. 2D disparity maps acquired by conventional matching algorithms do not match well with the original image profile in disparity edge regions because of the loss of fine and precise information in the regions. Therefore, in order to decrease the imprecision of disparity values and increase the quality of matching, a compact genetic algorithm is adapted for matching environments, and the adaptive window, which is controlled by the complexity of neighbor disparities in an abrupt disparity point is used. As the result, the proposed algorithm showed more correct and precise disparities were obtained than those by conventional matching methods with relaxation scheme.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1569-1580
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• 2004

One commonly used approach to deal with uncertainty is Bayesian network which represents joint probability distributions of domain. There are some attempts to team the structure of Bayesian networks automatically and recently many researchers design structures of Bayesian network using evolutionary algorithm. However, most of them use the only one fittest solution in the last generation. Because it is difficult to combine all the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In order to evaluate performance, we conduct experiments on learning Bayesian networks with artificially generated data from ASIA and ALARM networks. According to the experiments with diverse conditions, the proposed method provides with better robustness and adaptation for handling uncertainty.