• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.45-55
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• 2022

Currently, the Collective Control Algorithm is the most popular elevator algorithm. The Collective Control Algorithm allows the user to use the elevator when the direction of movement of the elevator and the direction of the user's destination are the same. However, the algorithm has a problem in that only one elevator responds to a user's call when the user's waiting time and using multiple elevators. To solve this problem, this paper proposes a new hybrid HRN algorithm based on the highest response ratio next (HRN) algorithm. In general, HRN Algorithm requires a user's boarding time and getting off time, but due to the nature of the elevator, it is difficult to predict the user's call in advance. Therefore, to overcome these limitations, this paper proposes Hybrid HRN Algorithm that considers the distance between the user's call location and the arrival location. This paper shows that Hybrid HRN Algorithm, proposed through experiments, has an average waiting time of 23.34 seconds, a standard deviation of 11.86, a total moving distance of 535.2m, a total operating time of 84sec, and a driving balance between the two elevators is 92m, which is superior to the previously suggested Collective Control, Zoning, and 3-Passage Algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.249-251
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• 2005

For walking patterns of biped robots, knee-bent patterns are used in most cases. However, humans are mostly walking with their knees nearly stretched. In this paper, a human-like walking algorithm using hybrid system is proposed for biped robots, The hybrid system consists of the logically constituted discrete system, in which the discrete states are defined by considering the walking characteristics, and the continuous state system used for motor control. It is shown that the proposed algorithm is effective by experimental studies.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.10
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• pp.1516-1523
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• 2003

An inverse radiation analysis is presented for the estimation of the boundary emissivities for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. The finite-volume method is employed to solve the radiative transfer equation for a two-dimensional irregular geometry. A hybrid genetic algorithm is proposed for improving the efficiency of the genetic algorithm and reducing the effects of genetic parameters on the performance of the genetic algorithm. After verifying the performance of the proposed hybrid genetic algorithm, it is applied to inverse radiation analysis in estimating the wall emissivities in a two-dimensional irregular medium when the measured temperatures are given at only four data positions. The effect of measurement errors on the estimation accuracy is examined.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1689-1699
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• 2006

A nonlinear channel blind equalizer by using a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, and a RBF network is presented. In this study, a hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA, and the relatively high accuracy and fast convergence of the method are achieved.

• Journal of Advanced Marine Engineering and Technology
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• v.19 no.3
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• pp.99-106
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• 1995

PWM(Pluse Width Modulation) induction motor drives are being used in greater numvers through a wide variety of industrial and commercial applications. In this paper, a new speed control algorithm (hybrid algorithm) for induction motor drives that uses regular sampled PWM and harmonic elimination PWM is presented. The hybrid algorithm in implemeted on the computer to obtain solutions from the calculation equations of the width of the pulses and the firing angles for the selected harmonic elimination. this paper describes the time delay effects and the suitable compensating methods moreover, optical transmission system for driving signals is proposed and is compared with general trnasmission system. The hybrid inverter was tested with induction motor, and these test results are shown that this hybrid inverter closely approximates and exhibits many of the desirable performance characteristic distortions and eliminated the objectionable harmonics. Finally, detailed experimental investigation of the proposed hybrid scheme in presented.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1304-1312
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• 2012

According to the successful development of hybrid vehicle, hybridization of construction equipments like excavator, wheel loader, and backhoe etc., is gaining increasing attention. However, hybridization of excavator and commercial vehicle is very different. Therefore a specialized energy management control algorithm for excavator should be developed. In this paper, hybridization of excavators is investigated and a new energy management control algorithm is proposed. Four control parameters, i.e., lower baseline, upper baseline, idling generation speed, and idling generation torque, are newly introduced and a new operating principle using those four control parameters is proposed. The use of Genetic Algorithm for the optimization of the four control parameters from the view point of minimization of fuel consumption for standard excavating operation is suggested. In order to verify the proposed algorithm, dedicated simulation program of hybrid excavator was developed. The proposed algorithm is applied to a specific hydraulic excavator and 20.7% improvement of fuel consumption is achieved.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• ETRI Journal
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• v.43 no.2
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• pp.260-271
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• 2021

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N2).

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.187-201
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• 1994

This paper introduces an effective hybridization of the usual simplex method and an interior point method in the convergent framework of Dembo and Sahi. We formulate a specific and detailed algorithm (HYBRID) and report the results of some preliminary testing on small dense problems for its viability. By piercing through the feasible region, the newly developed hybrid algorithm avoids the combinatorial structure of linear programs, and several other interesting and important characteristics of this algorithm are also discussed.