• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1210-1218
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• 2011

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.247-265
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• 2010

Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.33-49
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• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2824-2826
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• 2002

Immune system is an evolutionary biological system to protect innumerable foreign materials such as virus, germ cell, and etcetera. Immune algorithm is the modeling of this systems response that has adaptation and reliability when disturbance occur. In this paper, immune algorithm is proposed to control four wheels steering AGV(Automated Guided Vehicle) in container yard. The adaptive immune system is applied to the PID controller. For design the PID controller using immune algorithm, we tune PID parameters by off-line manner, in order to avoid the damage from abrupt control force. Repeatedly, the PID parameters are adjusted to be accurate by on-line fine tuner of immune algorithm. And then the computer simulation result from the viewpoint of yaw rate and lateral displacement are analyzed and compared with result of conventional PID controller.

• Animal cells and systems
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• v.1 no.4
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• pp.619-627
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• 1997

Maintenance of polymorphism in a two-locus system with two alleles under stabilizing selection has been tested by Monte-Carlo simulation. The effect of each allele was additive. Only gene x environment interactions and degree of genetic linkage between loci were considered. There were no other evolutionary forces acting except stabilizing selection. Fixation rates were influenced by the extent of environmental change and the degree of genetic linkage. In most cases, stabilizing selection depleted genetic variability when two loci have a lower degree of linkage (10 cM). When two loci are closely linked (0.1 cM), however, stabilizing selection promoted balanced heterozygotes in changing environments. Thus, environment-dependent selection and recombination rate are important parameters which should be incorporated into mechanisms of maintenance of genetic variability.

• ETRI Journal
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• v.39 no.6
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• pp.782-793
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• 2017

This paper considers the use of the Partial Transmit Sequence (PTS) technique to reduce the Peak-to-Average Power Ratio (PAPR) of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS) is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search-based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.624-636
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• 2006

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.97-100
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• 2000

In previous research of social dilemma, there is no extended game that the players can select one game out of some social dilemma games. We propose this kind of game as ＂Distributed Social Dilemma＂ In this game, each player tries to acquire the adaptive strategy throughout local interactions. We make use of GA as evolutionary operations. In this paper, our purpose is to examine how the game selection of players influences the evolution of cooperation in distributed social dilemma. In order to examine, we formulate distributed social dilemma by Game Theory and use agent-based simulation that each agent is regard as player

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.229-234
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• 2002

Immune system is an evolutionary biological system to protect Innumerable foreign materials such as virus, germ cell, and et cetera. Immune algorithm is the modeling of this system'response that has adaptation and reliableness when disturbance occur. In this paper, immune algorithm controller was proposed to control four wheels steering(4ws) Automated Guided vehicle(AGV) in container yard. And then the simulation result was analysed and compared with the results of NN-PID controller.

• Wind and Structures
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• v.18 no.1
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• pp.21-35
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• 2014

The latest developments in topology optimization are integrated with Computational Fluid Dynamics (CFD) for the conceptual design of building structures. The wind load on a building is simulated using CFD, and the structural response of the building is obtained from finite element analysis under the wind load obtained. Multiple wind directions are simulated within a single fluid domain by simply expanding the simulation domain. The bi-directional evolutionary structural optimization (BESO) algorithm with a scheme of material interpolation is extended for an automatic building topology optimization considering multiple wind loading cases. The proposed approach is demonstrated by a series of examples of optimum topology design of perimeter bracing systems of high-rise building structures.