• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.39-55
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• 2012

This paper presents an endosymbiotic evolutionary algorithm (EEA) to solve both problems of line balancing and model sequencing in a mixed-model two-sided assembly line (MMtAL) simultaneously. It is important to have a proper balancing and model sequencing for an efficient operation of MMtAL. EEA imitates the natural evolution process of endosymbionts, which is an extension of existing symbiotic evolutionary algorithms. It provides a proper balance between parallel search with the separated individuals representing partial solutions and integrated search with endosymbionts representing entire solutions. The strategy of localized coevolution and the concept of steady-state genetic algorithms are used to improve the search efficiency. The experimental results reveal that EEA is better than two compared symbiotic evolutionary algorithms as well as a traditional genetic algorithm in solution quality.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.439-442
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• 2003

In order to solve the difficulties of parameter settings in SA algorithm, an improved practical SA algorithm is proposed by employing the threading techniques, appropriate software structures, and dynamic adjustments of temperature parameters. Threads provide a mechanism to realize a parallel processing under a disperse environment by controlling the flux of internal information of an application. Thread services divide a process by multiple processes leading to parallel processing of information to access common data. Therefore, efficient search is achieved by multiple search processes, different initial conditions, and automatic temperature adjustments. The proposed are methods are evaluated, for three types of Traveling Salesman Problem (TSP) (random-tour, fractal-tour, and TSPLIB test data)are used for the performance evaluation. The experimental results show that the computational time is 5％ decreased comparing to conventional SA algorithm, furthermore there is no need for manual parameter settings. These results also demonstrate that the proposed method is applicable to real-world vehicle routing problems.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.669-679
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• 2013

This paper proposed a new VLSI (Very Large Scale Integrated Circuit) architecture for stereo matching in real time. We minimized the amount of calculation and the number of memory accesses through analyzing calculation of stereo matching. From this, we proposed a new stereo matching calculating cell and a new hardware architecture by expanding it in parallel, which concurrently calculates cost function for all pixels in a search range. After expanding it, we proposed a new hardware architecture to calculate cost function for 2-dimensional region. The implemented hardware can be operated with minimum 250Mhz clock frequence in FPGA (Field Programmable Gate Array) environment, and has the performance of 805fps in case of the search range of 64 pixels and the image size of $640{\times}480$.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1624-1627
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• 1997

The 6-DOF parallel manipulator is a closed-kindmatic chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. Because of its advantage, the parallel manipulator have been widely used in many engineering applications such as vehicle/flight driving simulators, rogot maniplators, attachment tool of machining centers, etc. However, the kinematic analysis for the implementation of a real-time controller has some problem because of the lack of an efficient lagorithm for solving its highly nonliner forward kinematic equation, which provides the translational and orientational attitudes of the moveable upper platform from the lenght of manipulator linkages. Generally, Newton-Raphson method has been widely sued to solve the forward kinematic problem but the effectiveness of this methodology depend on how to set initial values. This paper proposes a hybrid method using genetic algorithm(GA) and Newton-Raphson method to solve forward kinematics. That is, the initial values of forward kinematics solution are determined by adopting genetic algorithm which can search grobally optimal solutions. Since determining this values, the determined values are used in Newton-Raphson method for real time calcuation.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1476-1481
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• 2003

Kinematic calibration enhances absolute accuracy by compensating for the fabrication tolerances and installation errors. Effectiveness of calibration procedures depends greatly on the measurements performed. This paper investigates identifiable parameters and optimum postures for four different calibration procedures - measuring postures completely with inverse kinematic residuals, measuring postures completely with forward kinematics residuals, measuring only the three position components, and restraining the mobility of the end-effector using a constraint link. The study is performed for a six degree-of-freedom fully parallel HexaSlide type parallel manipulator, HSM. Results verify that all parameters are identifiable with complete posture measurements. For the case of position measurements, one and for the case of constraint link, three parameters were found non-identifiable. Selecting postures for measurement is also an important issue for efficient calibration procedure. Typically, the condition number of the identification Jacobian is minimized to find optimum postures. Optimal postures showed the same trend of orienting themselves on the boundaries of the search space.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.6
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• pp.608-618
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• 2000

We consider the problem of placing resources in a distributed computing system so that certain performance requirements may be met while minimizing the number of required resource copies, irrespective of node or link failures. To meet the requirements for high performance and high availability, minimum number of resource copies should be placed in such a way that each node has at least two copies on the node or its neighbor nodes. This is called the fault-tolerant resource placement problem in this paper. The structure of a parallel or a distributed computing system is represented by a graph. The fault-tolerant placement problem is first transformed into the problem of finding the smallest fault-tolerant dominating set in a graph. The dominating set problem is known to be NP-complete. In this paper, searching for the smallest fault-tolerant dominating set is formulated as a state-space search problem, which is then solved optimally with the well-known A* algorithm. To speed up the search, we derive heuristic information by analyzing the properties of fault-tolerant dominating sets. Some experimental results on various regular and random graphs show that the search time can be reduced dramatically using the heuristic information.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1239-1244
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• 2015

In this paper, a new method for finding optimal and short span in Convolutional Self-Doubly Orthogonal(CDO) codes are proposed. This new algorithm based on Parallel Implicitly-Exhaustive search, where we applied dynamic search space reduction methods in order to reduce computational time for finding Optimal Span for R=1/2 rate CDO codes. The simulation results shows that speedup and error correction performance of the new algorithm is better.