• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.401-409
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• 2023

This study compared and analyzed the performance of a distributed area search algorithm and a distributed probability search algorithm based on information exchange between ships. The distributed algorithm is a method that can search for an optimal avoidance route based on information exchange between ships. In the distributed area search algorithm, only a ship with the maximum cost reduction among neighboring ships has priority, so the next expected location can be changed. The distributed stochastic search algorithm allows a non-optimal value to be searched with a certain probability so that a new value can be searched. A circular-based ship collision avoidance model was used for the ship-to-ship collision avoidance experiment. The experimental method simulated the distributed area search algorithm and the distributed stochastic search algorithm while increasing the number of ships from 2 to 50 that were the same distance from the center of the circle. The calculation time required for each algorithm, sailing distance, and number of message exchanges were compared and analyzed. As a result of the experiment, the DSSA(Distributed Stochastic Search Algorithm) recorded a 25%calculation time, 88% navigation distance, and 84% of number of message exchange rate compared to DLSA.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.811-812
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• 2008

In this paper, a modified cross search algorithm for fast block matching motion estimation is proposed. Various Motion Estimation (ME) algorithms have been proposed since ME requires large computational complexity. The proposed algorithm employs Modified Cross Search Pattern (MCSP) to search the motion vector. Efficient compression can be achieved since Modified Cross Search Algorithm (MCSA) simplifies the search pattern to reduce the computational complexity. The experimental results show that proposed algorithm reduces the search points up to 29% compared to conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.441-446
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• 2010

Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

• Coupled systems mechanics
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• v.13 no.1
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• pp.1-19
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• 2024

The effectiveness of coupling with a bolted rim is assessed in this research using a newly designed optimization algorithm. The current study, which is provided here, evaluates 10 contemporary metaheuristic approaches for enhancing the coupling with bolted rim design problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The contribution of the paper isto optimize the coupling with bolted rim problem by comparing these 10 algorithms and to find which algorithm gives the best optimized result. These algorithm's performance is evaluated statistically and subjectively.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1097-1100
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• 1999

Due to the significant computation of full search in motion estimation, extensive research in fast motion estimation algorithms has been carried out. However, most of the algorithms have the degradation in predicted images compared with the full search algorithm. To reduce an amount of significant computation while keeping the same prediction quality of the full search, we propose a fast block-matching algorithm based on gradient magnitude of reference block without any degradation of predicted image. By using Taylor series expansion, we show that the block matching errors between reference block and candidate block are proportional to the gradient magnitude of matching block. With the derived result, we propose fast full search algorithm with adaptively determined scan direction in the block matching. Experimentally, our proposed algorithm is very efficient in terms of computational speedup and has the smallest computation among all the conventional full search algorithms. Therefore, our algorithm is useful in VLSI implementation of video encoder requiring real-time application.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.75-84
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• 2006

With considering center-biased characteristic, NTSS(New Three-Step Search Algorithm) can improve the performance of TSS(Three-Step Search Algorithm) which is one of the most popular fast block matching algorithms(BMA) to search a motion vector in a video sequence. Although NTSS has generally better Quality than TSS for a small motion sequence, it is hard to say that NTSS can provide better quality than TSS for a large motion sequence. It even deteriorates the quality to increase a search window size using NTSS. In order to address this drawback, this paper aims to develop a new adaptive window size-based three step search scheme, called AWTSS, which can improve quality at various window sizes in both the small and the large motion video sequences. In this scheme, the search window size is dynamically changed to improve coding efficiency according to the characteristic of motion vectors. AWTSS can improve the video quality more than 0.5dB in case of large motion with keeping the same quality in case of small motion.

• Steel and Composite Structures
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• v.45 no.4
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• pp.579-590
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• 2022

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated Squirrel Search Algorithm, which consists of an association of the well-established neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (Squirrel Search Algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the Squirrel Search Algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.451-454
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• 2002

The fiber optic communication technology is considered as a key solution for the future communication. However the assembly of the fiber optic components highly depends on manual or semi-automated alignment process. And the light search algorithm is recognized an important factor to reduce the manufacturing process time. Therefore this paper investigates optimal search algorithm for the automatic alignment of fiber optic components. The experiments show the effectiveness of Hill Climbing Search, Adaptive Hill Climbing Search and Steepest Search algorithms, in a view of process time.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.295-298
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• 2002

This paper presents a fast block-matching algorithm to improve the conventional Three-Step Search (TSS) based method. The proposed Comparison Fast Block Matching Algorithm (CFBMA) begins with DAB for adaptive search range to choose searching method, and searches a part of search window that has high possibility of motion vector like other partial search algorithms. The CFBMA also considers the opposite direction to reduce local minimum, which is ignored in almost conventional based partial search algorithms. CFBMA uses the summation half-stop technique to reduce the computational load. Experimental results show that the proposed algorithm achieves the high computational complexity compression effect and very close or better image quality compared with TSS, SES, NTSS based partial search algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.8
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• pp.1281-1286
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• 2003

A line search algorithm to increase a convergence in Newton's method is developed and applied to nonlinear finite element analysis. The algorithm is based on the slack line search theory which is an efficient algorithm to determine initial acceleration coefficient, variable backtracking algorithm proposed by some researchers, and convergence criterion based on residual norm. Also, it is capable of avoiding exceptional diverging conditions. Developed program is tested in metal forming simulation such as forging and ring rolling. Numerical result shows the validity of the algorithm for a highly nonlinear system .