• 한국멀티미디어학회논문지
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• 제5권6호
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• pp.745-752
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• 2002

도로에서의 최적 경로 탐색은 출발지와 목적지의 위치를 알고 있는 경우로서 탐색에 대한 일종의 사전 지식을 가진 탐색으로 A* 알고리즘이 널리 사용되고 있다. 단방향 A* 알고리즘은 최적의 경로를 보장해 주는 반면 탐색 시간이 많이 소요되고 양방향 A* 알고리즘은 최적 경로를 보장해 주지 못하거나 최적 경로 보장을 위해서는 오히려 단방향 A* 보다 탐색 시간이 더 많이 소요될 수도 있다. 본 논문에서는 탐색 시간이 우수하며 최적 경로를 보장하는 새로운 양방향 A* 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘의 효용성을 확인하기 위하여 실제 도로에 적용한 격과 정확한 최적 경로를 탐색하고 탐색 시간도 매우 우수한 것으로 확인되었다.

• 제어로봇시스템학회논문지
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• 제3권2호
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• pp.156-161
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• 1997

In this thesis, based on Genetic Algorithm, a new route search algorithm is presented to search an optimal route between the origin and the destination in intelligent route guidance systems in order to minimize the route traveling time. The proposed algorithm is effectively employed to complex road networks which have diverse turn constrains, time-delay constraints due to cross signals, and stochastic traffic volume. The algorithm is also shown to significantly promote search efficiency by changing the population size of path individuals that exist in each generation through the concept of age and lifetime to each path individual. A virtual road-traffic network with various turn constraints and traffic volume is simulated, where the suggested algorithm promptly produces not only an optimal route to minimize the route cost but also the estimated travel time for any pair of the origin and the destination, while effectively avoiding turn constraints and traffic jam.

• 한국해양공학회지
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• 제20권1호
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• pp.69-76
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• 2006

The design of efficient search path to maximize the Cumulative Detection Probability(CDP) is mainly dependent on experience and intuition when searcher detect the target using SONAR in the ocean. Recently with the advance of modeling and simulation method, it has been possible to access the optimization problems more systematically. In this paper, a method for the optimal search path calculation is developed based on the combination of the genetic algorithm and the calculation algorithm for detection range. We consider the discrete system for search path, space, and time, and use the movement direction of the SONAR for the gene of the genetic algorithm. The developed algorithm, OASPP(Optimal Acoustic Search Path Planning), is shown to be effective, via a simulation, finding the optimal search path for the case when the intuitive solution exists. Also, OASPP is compared with other algorithms for the measure of efficiency to maximize CDP.

• 한국산업융합학회 논문집
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• 제24권6_2호
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• pp.891-897
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• 2021

In this paper, we propose a destination optimal route algorithm for providing route finding service for the transportation handicapped by using the multi-criteria decision-making technique and the modified A-STAR optimal route search algorithm. This is a method to set the route to the destination centering on safety by replacing the distance cost of the existing A-STAR optimal route search algorithm with the safety cost calculated through AHP/TOPSIS analysis. To this end, 10 factors such as road damage, curb, and road hole were first classified as poor road factors that hinder road driving, and then pairwise comparison of AHP was analyzed and then defined as the weight of TOPSIS. Afterwards, the degree of driving safety was quantified for a certain road section in Busan through TOPSIS analysis, and the development of an optimal route search algorithm for the transportation handicapped that replaces the distance cost with safety in the finally modified A-STAR optimal route algorithm was completed.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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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.

• 한국군사과학기술학회지
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• 제13권1호
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• pp.1-8
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• 2010

This paper presents global and optimal solution for weapon assignment problems using the Munkres assignment algorithm. We propose a new modeling method of weapon assignment problems concerning some constraints of weapon systems. In this paper, we compares the Munkres weapon assignment algorithm with two other algorithms employing a search tree model in terms of computational complexity and performance. One is an optimal algorithm using exhausted search and the other is a greedy algorithm which selects the first search result as a solution. The experiment results show that the Munkres weapon assignment algorithm has better performance and less computational complexity in comparison with the two other algorithms.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.554-558
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• 2001

In this study, a hybrid method is used to search the pseudo-optimal solution for the I-dimentional nesting problem. This method is composed of the genetic algorithm for the global search and a simple heuristic one for the local search near the pseudo optimal solution. Several simulation results show that the hybrid method gives very satisfactory results.

• 전기학회논문지
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• 제59권5호
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• pp.866-870
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• 2010

This paper presents a application of Harmony Search (HS) algorithm for optimal placement of distributed generations(DGs) in distribution systems. In optimization procedure, the HS algorithm denotes the searching ability for the global optimal solution with simple coding of the iteration procedure, and shows the fast convergence characteristics for getting solutions. The HS algorithm is tested on 9 buses and 69 buses distribution systems, and the results prove its effectiveness to determine appropriate placement points of DGs and reducing amount of active power without the occurrence of any mis-determination in selection of its capacity.

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.76-82
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• 2001

This paper presents a approach for a service restoration and optimal reconfiguration of distribution network using Genetic algorithm(GA) and Tabu search(TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve in short times, because distribution network supplies power for customers combined with many tie-line switches and sectionalizing switches. Furthermore, the solutions of these problems have to satisfy radial operation conditions and reliability indices. To overcome these time consuming and sub-optimal problem characteristics, this paper applied Genetic-Tabu algorithm. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to complement the weak points of each algorithm. The case studies with 7 bus distribution network showed that not the loss reduction but also the reliability cost should be considered to achieve the economic service restoration and reconfiguration in the distribution network. The results of suggested Genetic-Tabu algorithm and simple Genetic algorithm are compared in the case study also.

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.116-124
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• 1999

In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.