• The Journal of the Acoustical Society of Korea
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• v.16 no.3E
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• pp.62-68
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• 1997

In this paper, a modified adaptive sign (MAS) algorithm based on a mixed norm error criterion is proposed. The mixed norm error criterion of be minimized is constructed as a combined convex function of the mean-absolute error and the mean-absolute error to the third power. A convergence analysis of the MAS algorithm is also presented. Under a set of mild assumptions, a set of nonlinear evolution equations that characterizes the statistical mean and mean-squared behavior of the algorithm is derived. Computed simulations are carried out to verify the validity of our derivations.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.820-822
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• 1996

This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.323-328
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• 2011

In real system application, the 3-D obstacle avoidance system for the autonomous control of the underwater flight vehicle (UFV) operates with the following problems: the sonar offers the range/bearing information of obstacles in a local detection area, it requires the system that has reduced acoustic noise and power consumption in terms of the autonomous underwater vehicle (AUV), it has the UFV operation constraints such as maximum pitch and depth, and it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an intelligent 3-D obstacle avoidance algorithm using the evolution strategy (ES) and the fuzzy logic controller (FLC), is proposed. To verify the performance of the proposed algorithm, the 3-D obstacle avoidance of UFV is performed. Simulation results show that the proposed algorithm effectively solves the problems in the real system application.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.635-640
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• 2009

In real system application, the obstacle avoidance system for the autonomous control of the underwater flight vehicle (UFV) operates with the following problems: it has local information because the sonar can only offer the obstacle information in a local detection area, it requires a continuous control input because the system that has reduced acoustic noise and power consumption is necessary, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an intelligent obstacle avoidance algorithm using the evolution strategy (ES) and the fuzzy logic controller (FLC), is proposed. To verify the performance of the proposed algorithm, the obstacle avoidance of UFV is performed. Simulation results show that the proposed algorithm effectively solves the problems in the real system application.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.498-503
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• 1994

The paper demonstrates that holon networks can be used effectively for identification of nonlinear dynamical systems. The emphasis of the paper is on modeling of complicated systems which have a great deal of uncertainty and unknown interactions between their elements and parameters. The concept of applying a quantitative model building, for example, to environmental or ecological systems is not new. In a previous paper we presented a holon network model as an another alternative to quantitative modeling. Holon networks have a hierarchical construction where each level of hierarchy consists of networks with reciprocal actions among their elements. The networks are able to evolve by self-organizing their structure and adapt their parameters to environments. This was achieved by an autonomous decentralized adaptation algorithm. In this paper we propose a new emergent evolution algorithm. In this algorithm the initial holon networks consists of only a few elements and it grows gradually with each new observation in order to fit their function to the environment. Some examples show that this algorithm can lead to a network structure which has sufficient flexibility and adapts well to the environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3117-3132
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• 2012

The tree topology in multicast systems has high transmission efficiency, low latency, but poor resilience to node failures. In our work, some nodes are selected as backbone nodes to construct a tree-like core overlay. Backbone nodes are reliable enough and have strong upload capacity as well, which is helpful to overcome the shortcomings of tree topology. The core overlay is organized into a spanning tree while the whole overlay is of mesh-like topology. This paper focuses on improving the performance of the application-layer multicast overlay by optimizing the core overlay which is periodically adjusted with the proposed optimization algorithm. Our approach is to construct the overlay tree based on the out-degree weighted reliability where the reliability of a node is weighted by its upload bandwidth (out-degree). There is no illegal solution during the evolution which ensures the evolution efficiency. Simulation results show that the proposed approach greatly enhances the reliability of the tree-like core overlay systems and achieves shorter delay simultaneously. Its reliability performance is better than the reliability-first algorithm and its delay is very close to that of the degree-first algorithm. The complexity of the proposed algorithm is acceptable for application. Therefore the proposed approach is efficient for the topology optimization of a real multicast overlay.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.379-403
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• 2012

In recent years, the need for optimal design of structures under time-history loading aroused great attention in researchers. The main problem in this field is the extremely high computational demand of time-history analyses, which may convert the solution algorithm to an illogical one. In this paper, a new framework is developed to solve the size optimization problem of steel truss structures subjected to ground motions. In order to solve this problem, the covariance matrix adaptation evolution strategy algorithm is employed for the optimization procedure, while a generalized regression neural network is utilized as a meta-model for fitness approximation. Moreover, the computational cost of time-history analysis is decreased through a wavelet analysis. Capability and efficiency of the proposed framework is investigated via two design examples, comprising of a tower truss and a footbridge truss.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.971-973
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• 1993

Deflection Yoke(the following, DY) is the important electric device of CRT which deflects R, G, B beans influencing magnetic field produced by yoke coils. Recently, DY is designed to the saddle/saddle type of coils, being proposed for high-definite and high-efficient CRT. This paper presents the optimization of pin-sectioned saddle coil's shape for minimizing gap between desired and practical deflections of electron beams by using Genetic Algorithm. Evolution Startegy is utilized in this paper, since evolution strategy is a kind of genetic algorithms finding the optimized values by choicing the better generation with comparing the parents and their children. Here, the children are generated by only mutations from the normal random variables. Evolution strategy has shown better powerful converge rate than the other genetic algorithms becuase of using only the mutation-operator.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.288-291
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• 2008

Physics based Cellular Automata model is developed and implemented into FEM code. CA model can predict microstructure evolution based on physical phenomena, such as hardening, recovery and recrystallization. This paper outlines the methodology to determine the materials constants for these different phenomena from simpler measurements.