• Journal of Information Processing Systems
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• 제14권5호
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3512-3528
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• 2016

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

• 한국지능시스템학회논문지
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• 제18권5호
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• pp.649-658
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• 2008

본 논문에서는 기존의 PSO(Conventional Particle Swarm Optimization : CPSO) 알고리즘에서 매 반복횟수마다 매개변수 값을 적응적으로 변화시키는 적응 PSO(APSO) 알고리즘을 제안하였다. 본 논문에서 제안한 APSO의 성능을 평가하기 위해 De Jong함수, Ackley 함수, Davis 함수 Griewank 함수 등의 최소화 문제에 적용하여 실수형 유전알고리즘과 그 결과를 비교하여, 제안한 알고리즘에 대한 우수성을 증명하였다. 그리고 자계계측기와 설문지를 통해 얻은 전자계 노출량에 대한 실측데이터를 이용하여 개인생활 자계노출식 개발에 제안한 APSO를 적용하여 그 우수성을 입증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2126-2131
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• 2005

Interactions of a humanoid with a human are important, when the humanoid is requested to provide people with human-friendly services in unknown or uncertain environment. Such interactions may require more complicated and human-like behaviors from the humanoid. In this work the arm motions of a human are discussed as the early stage of human motion imitation by a humanoid. A motion capture system is used to obtain human-friendly arm motions as references. However the captured motions may not be applied directly to the humanoid, since the differences in geometric or dynamics aspects as length, mass, degrees of freedom, and kinematics and dynamics capabilities exist between the humanoid and the human. To overcome this difficulty a method to adapt captured motions to a humanoid is developed. The geometric difference in the arm length is resolved by scaling the arm length of the humanoid with a constant. Using the scaled geometry of the humanoid the imitation of actor's arm motions is achieved by solving an inverse kinematics problem formulated using optimization. The errors between the captured trajectories of actor arms and the approximated trajectories of humanoid arms are minimized. Such dynamics capabilities of the joint motors as limits of joint position, velocity and acceleration are also imposed on the optimization problem. Two motions of one hand waiving and performing a statement in sign language are imitated by a humanoid through dynamics simulation.

• 한국ITS학회 논문지
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• 제15권4호
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• pp.143-152
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• 2016

본 논문에서는 이동통신대역에 사용 가능한 MMIC(Monolithic Microwave Integrated Circuits) 이중평형 주파수 혼합기(Double Balanced Mixer)를 설계하고 LO 전력 최적화에 관한 연구를 다룬다. 본 논문에서 제안한 MMIC 이중평형 주파수 혼합기의 칩 크기는 $4{\times}4$[$mm^2$]이며, GaAs 기판을 사용한다. LO power에 대한 최적화 연구는 입력신호의 선형성에 대한 Input IP3(IIP3)에 대해서 진행하며, LO power+16 dBm을 인가했을 때 IIP3성분이 약 23.2dBm을 보여 가장 우수한 특성을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• 한국지능시스템학회논문지
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• 제18권2호
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• pp.212-215
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• 2008

본 논문에서 개선된 Dijkstra 알고리즘과 입자 군집 최적화를 이용한 최적 경로 계획 알고리즘을 제안한다. 최적의 경로를 구하기 위해 우선 이동 로봇 공간에서 MAKLINK를 작성하고 MAKLINK와 관련한 그래프를 얻는다. 여기서 MAKLINK는 장애물의 꼭지점을 연결하면서 볼록집합이 만들어지도록 하는 모서리의 집합을 의미한다. 얻은 그래프에서 출발점과 도착점을 포함하여 Dijkstra 알고리즘을 이용하여 최소 비용 최적 경로를 얻고 이 최적의 경로에서 개선된 Dijkstra경로를 얻는다. 마지막으로 개선된 Dijkstra경로에서 입자 군집 최적화를 적용하여 최적의 경로를 얻는다. 제안된 방법이 논문[1]에 나온 결과보다 더 좋은 성능을 갖는다는 것을 실험을 통해 입증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.321-326
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.