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지금까지의 최적경로탐색 알고리즘은 노드표지와 링크표지를 적용하는 방안이 소개되었다. 노드표지는 2개의 노드를 최적경로 탐색과정에서 동시에 고려한다. 링크표지는 2개의 링크를 탐색과정에서 동시에 고려한다. 본 연구는 2개의 회전을 탐색과정에서 동시에 고려하는 회전 표지기반 최적경로탐색기법을 제안한다. 회전표지기반 최적경로탐색은 2개의 회전을 탐색과 정에서 고려하기 때문에 Bellman(1957)의 최적원리에 근거한 동적프로그래밍의 최적해가 보장 된다. 한편 회전표지기반 최적경로탐색은 링크표지의 극한 접근 개념을 동일하게 적용하기 때 문에 링크표지를 적용하는 장점을 수용할 수 있다. 따라서 네트워크의 확장없이 노드는 복수 의 방문이 허용되면서 링크는 방문이 허용되지 않는 합리적 순환통행을 반영하는 것이 가능하 다. 특히 2개의 연속회전에서 나타나는 추가적인 비용구조를 반영하는 특성이 포함되어 통행 비용함수의 구조를 보다 유연하게 표현하는 것이 가능하다. 교통카드 단말기로 구성된 수도권 도시철도 네트워크를 대상으로 시행된 사례연구는 연속된 보행환승이 나타나는 통행에 대한 심리적 저항감을 나타내는 파라메타를 Turn Label 최적경로탐색에 도입하여 연구의 현실적 확 장성을 검토하였다. 연속회전에 대한 심리적 저항값이 커지면서 통행시간 및 거리가 늘어나도 연속된 환승을 우회하는 방안으로 시뮬레이션 결과가 도출되어 Turn Label의 비용구조를 반영 하는 것이 확인되었다. 향후 도로교통망 및 대중교통망의 통행비용함수에 대해 다양성을 확보 하기 위한 추가적인 연구의 진행이 필요하다.

The most optimal route-search algorithm thus far has introduced a method of applying node labels and link labels. Node labels consider two nodes simultaneously in the optimal route-search process, while link labels consider two links simultaneously. This study proposes a turn-label-based optimal route-search technique that considers two turns simultaneously in the process. Turn-label-based optimal route search guarantees the optimal solution of dynamic programming based on Bellman’s principle as it considers a two-turn search process. Turn-label-based optimal route search can accommodate the advantages of applying link labels because the concept of approaching the limit of link labels is applied equally. Therefore, it is possible to reflect rational cyclic traffic where nodes allow multiple visits without expanding the network, while links do not allow visits. In particular, it reflects the additional cost structure that appears in two consecutive turns, making it possible to express the structure of the travel-cost function more flexibly. A case study was conducted on the metropolitan urban railway network consisting of transportation card terminal readers, aiming to examine the scalability of the research by introducing parameters that reflect psychological resistance in travel with continuous pedestrian transfers into turn label optimal path search. Simulation results showed that it is possible to avoid conservative transfers even if the travel time and distance increase as the psychological resistance value for continuous turns increases, confirming the need to reflect the cost structure of turn labels. Nevertheless, further research is needed to secure diversity in the travel-cost functions of road and public-transportation networks.

기존의 Node-to-Node기반 최적경로탐색은 기점노드에서 모든 종점노드도착조건이 성립되 는 가정으로 구축되었다. 최근 적응적 경로탐색의 등장으로 Node-to-Node 경로탐색은 최적해 를 도출하지 못하는 한계가 존재한다. 따라서 교통정보를 링크에서 실시간 반영하기 위한 Node-to-Link(또는 Link-to-Node; NL) 문제에 필요성이 대두되고 있다. 본 연구는 Node-to-Link의 최적 해법을 구축하는 방안으로서 링크표지와 비가산성경로비용이 존재하는 네트워크를 가정 한다. 링크표지는 회전페널티가 존재하는 교차지점에서 네트워크의 원형을 유지하게 한다. 비 가산성경로비용의 포함은 최적경로를 도출하기 위해서 M-유사경로의 열거를 필요로 한다. 본 연구는 진입링크기반 네트워크 변형기법에서 링크표지를 통하여 루프를 통제하며 최종링크까 지 최적해를 보장하기 위한 방향삭제와 회전금지를 제안하였다. 사례연구를 통해 제안된 방법 이 경험적 최적해를 도출하는 것으로 파악되었다. 향후 대규모 네트워크에서 검증작업의 필요 성을 언급하며 마무리 하였다.

Existing node-to-node based optimal path searching is built on the assumption that all destination nodes can be arrived at from an origin node. However, the recent appearance of the adaptive path search algorithm has meant that the optimal path solution cannot be derived in node-to-node path search. In order to reflect transportation data at the links in real-time, the necessity of the node-to-link (or link-to-node; NL) problem is being recognized. This research assumes existence of a network with link-label and non-additive path costs as a solution to the node-to-link optimal path problem. At the intersections in which the link-label has a turn penalty, the network retains its shape. Non-additive path cost requires that M-similar paths be enumerated so that the ideal path can be ascertained. In this, the research proposes direction deletion and turn restriction so that regulation of the loop in the link-label entry-link-based network transformation method will ensure that an optimal solution is derived up until the final link. Using this method on a case study shows that the proposed method derives the optimal solution through learning. The research concludes by bringing to light the necessity of verification in large-scale networks.

In this study, a drone-based optimal path simulator was developed to evaluate four path finding algorithms-Dijkstra, A^*, D^* Lite, and C hristofides-for agricultural pest control missions. The simulator was tested under two spatial distribution conditions (scattered and clustered) with varying numbers of target points (50, 100, and 150). Each algorithm was assessed using four performance metrics: energy consumption, total travel distance, simulation time, and path computation time. A weighted scoring system was applied to derive a comprehensive performance score. The simulation results showed that the Christofides algorithm consistently outperformed other algorithms in large-scale and complex environments, achieving perfect scores in scenarios with 100 and 150 target points, regardless of distribution type. The A^* algorithm exhibited balanced and effective performance in small-scale or clustered environments, particularly with 50 target points, and maintained moderate performance at medium scale. These findings highlight the importance of algorithm selection based on both spatial distribution and problem scale. The Christofides algorithm is recommended for large-scale optimization tasks, while A^* is suitable for small-scale or dense environments.

Optimal path planning refers to find the safe route to the destination at a low cost, is a major problem with regard to autonomous navigation. Sampling Based Planning(SBP) approaches, such as Rapidly-exploring Random Tree Star($RRT^*$), are the most influential algorithm in path planning due to their relatively small calculations and scalability to high-dimensional problems. $RRT^*$-Smart introduced path optimization and biased sampling techniques into $RRT^*$ to increase convergent rate. This paper presents an improvement plan that has changed the biased sampling method to increase the initial convergent rate of the $RRT^*$-Smart, which is specified as m$RRT^*$-Smart. With comparison among $RRT^*$, $RRT^*$-Smart and m$RRT^*$-Smart in 2 & 3-D environments, m$RRT^*$-Smart showed similar or increased initial convergent rate than $RRT^*$ and $RRT^*$-Smart.

본 논문에서는 시간 가중치를 적용하여 각 노드들 간의 중간지점까지의 최적 경로 탐색 기법을 제안한다. 중간지 점을 이용하는 서비스들은 주로 사용자들의 위치를 기반으로 하여 제공한다. 위치 기반 탐색 방법은 단순히 위치에 대해 서만 고려하기 때문에 시간의 측면에서 효율적이지 못하다는 문제점이 있다. 제안된 방법은 기존의 위치 기반 탐색 방법 의 문제점을 해결하기 위해 사용자들의 위치와 중간지점까지의 소요시간을 반영함으로써 각 노드와 중간지점까지의 가 중치를 설정하고, 이를 활용하여 최적의 경로를 탐색할 수 있다. 또한, 탐색의 효율성을 증대하기 위해 주어지는 정보들 에 적응적으로 가중치를 설정함으로써 높은 정확성을 갖도록 한다. 실험 결과, 제안된 방법은 기존 방법에 비해 중간지 점까지의 최적 경로를 효과적으로 찾을 수 있음을 확인하였다.

In this paper, we propose an optimal path search algorithm between each node and midpoint that applies the time weighting. Services for using a location of mid point usually provide a mid point location-based on the location of users. There is a problem that is not efficient in terms of time because a location-based search method is only considered for location. To solve the problem of the existing location-based search method, the proposed algorithm sets the weights between each node and midpoint by reflecting user's location information and required time. Then, by utilizing that, it is possible to search for an optimum path. In addition, to increase the efficiency of the search, it ensures high accuracy by setting weights adaptively to the information given. Experimental results show that the proposed algorithm is able to find the optimal path to the midpoint compared with the existing method.

In this paper, we propose a Hopfield model for solving the part-matching in case that is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and total path of part-connections. Therefore, this kind of problem is referred to as a combinatiorial optimization problem. First of all, we review the theoretical basis for Hopfield model and present two optimal algorithms of part-matching. The first algorithm is Traveling Salesman Problem(TSP) which improved the original and the second algorithm is Wdighted Matching Problem (WMP). Finally, we show demonstration through com- puter simulation and analyze the stability and feasibility of the generated solutions for the proposed con- nection methods. Therefore, we prove that the second algorithm is better than the first algorithm.

Live physiological monitoring of basketball players during events can help maximize athlete performance while preventing injury and enhance TV broadcast services. However, the harsh conditions in the basketball court pose many challenges. In this paper, we propose an improved optimal path and energy efficiency sensor routing algorithm for athletes’ monitoring services. The goal is to pave the way to the realisation of real-time athlete monitoring systems. In our scheme, the basketball court diagram and player default positions are given for the connectivity data collection. Then the radio signal propagation model is set up. Additionally multi-hop routing algorithms for real-time monitoring are analysed. Finally the IOPEESRA algorithm is proposed. The simulation results show that our proposed algorithm has a good performance in terms of average energy comsumption compared with the algorithm of M-LEACH and ARPEES.

Choosing a reasonable Rescue route guarantees emergency rescue command center to start the rescue work timely and effectively. In this paper, use the optimal path choice of public emergencies as an entry point of research build a post-disaster Ad Hoc net through wireless sensor technology, use the limited sensor resources to meet the rescue vehicle path optimization problem. Ant Colony Algorithm absorb the behavior of insect kingdom ant, use internal search mechanism to introduce sensor networks useful information, Fully consider the impact of the road traffic rescue vehicle travel time because of changes the traffic capacity, from rescue vehicles transport resources’ two factors -the timeliness and economy, maximize meet the transport requirements of post-disaster emergency relief material to reduce the aid does not lead to timely the direct and indirect losses, maximize meet the transport requirements of post-disaster emergency relief material to reduce the aid does not lead to timely the direct and indirect losses. The simulation results show that the design of the optimal path to solve the disaster relief vehicles have obvious advantages and important practical significance.It Improved ant colony algorithm can quickly build a optimal routing, improve the efficiency of path optimization, effectively reduce the node energy consumption and ensure the whole network energy balance.

The traditional genetic algorithm randomly selects nodes in two chromosomes for crossover operation, which may result in individuals of disconnected or loop circuit and lead to issues as meaningless crossover operations. In order to increase the diversity of the population and prevent the occurrence of premature mutation algorithm which might cause local convergence, this essay presents a new urban traffic optimal path planning method. Initialized from the improvement of population genetic algorithm, it designs the fitness function and optimizes crossover and mutation operators so that the optimal or near-optimal solution can be quickly figured out. Moreover, the Matlab software simulation test exhibits the feasibility and effectiveness of the method.

본 논문에서는 위성과 모바일기기 간의 전파수신감도를 활용한 최적경로탐색 알고리즘을 제안한다. 일반적인경로탐색은 최단경로를 기반으로 하기 때문에 위성을 이용하는 모바일기기의 안정적인 멀티미디어 서비스 제공이 어렵다. 제안된 알고리즘은 전파수신감도를 기반으로 하는 경로탐색을 통해 안정적인 통신환경의 제공을 목적으로 한다. 위성통신에서 수신환경에 따라 전파수신감도 변화가 심하기 때문에 수신환경의 특성을 정확하게 분석하는 것이 매우 중요하다. 전파감쇠 요인으로 대기감쇠, 수풀감쇠, 건물에 의한 감쇠 등을 적용하여 시뮬레이션을 통해 분석하고 전파수신감도로 활용한다. 제안된 알고리즘은 도심지에서 위성을 활용한 안정적인 멀티미디어 서비스 제공을 위한 최적경로를 찾을 수 있다.

In this paper, we propose an optimal path search algorithm that applies the received signal strength between a mobile device and a satellite. Because the common path search algorithm is only based on the shortest path search, it is difficult to provide stable multimedia services for the satellite mobile devices. The proposed algorithm provides the stable communication environment for the satellite mobile devices based on received signal strength. In Satellite communications, changes in the radio quality are severe depending on the receiving environment. Therefore, an accurate analysis of the receiving environment characteristics is very important for providing stable multimedia services of satellite communications. The causes of radio attenuation are atmosphere attenuation, vegetation attenuation and buildings attenuation. These factors were applied to analyze the received signal strength. The proposed algorithm can search the optimal path in urban for stable satellite multimedia services.

본 논문에서는 GIS와 네트웍 알고리듬을 이용하여 통신관망의 설계를 최적화하였다. 구체적이고 계량적인 분석을 위해 신규로 조성되는 대규모의 도시개발지역을 대상으로 하여 기존의 설계방식에 의해 산출된 통신관망과 최적경로 알고리듬을 이용한 통신관망과의 건설비용을 비교하였다. 본 연구의 결과 GIS와 최적경로 알고리듬을 적용한 설계 결과가 기존의 설계방법에 비해 상당한 비용절감 효과가 있음을 보여준다. 연구 대상지역에서 기존설계에 의해 산출된 통신관로의 거리는 5,267m이며 최적경로를 적용한 결과는 4,807m로 460m(9.5%)의 차이를 보였다. 결과를 비용으로 산정했을 경우 약 19,469천원(8.7%)의 비용절감 효과를 보였다. 본 연구에서 제시된 최적화 기법은 기존 설계기법에 비해 통신관망의 공사비의 절감 이외에 설계과정의 표준화 및 자동화가 가능하므로 이를 이용한 자동설계 시스템 개발로 설계비용과 인력이동에 따른 설계업무 적응기간 단축과 인건비 절감이 가능하며, 또한 설계 결과에 대한 객관적 검증이 가능할 것으로 판단된다.

In this paper, several network algorithms were tested to find an optimal one for telecommunication network design. Algorithms such as Dijkstra's shortest path algorithm, Ford-Bellman's shortest path algorithm, Prim's minimum spanning tree algorithm, and Kruscal's minimum spanning tree algorithm were reviewed and compared in terms of feasibility and resulted network construction cost. Then an optimal algorithm that is most suitable for telecommunication network design was selected. For more specific and quantitative analysis of the selected algorithm, it was implemented to a real telecommunication network construction site. The analyzed results showed that the suggested design method when compared with the conventional one, reduced the network construction cost considerably. The total network length estimated by the conventional method were 5267 meters while the suggested method resulted in 4807 meters. Thus the new method reduced the total network length by 8.7 percent which is equivalent to 97,469,000 Won of construction cost. Considering the frequent telecommunication network constructions, due to new urban developments in the nation, the economic benefit of the suggested telecommunication network design method will be significant. In addition to the construction cost savings, the suggested telecommunication network design procedure possesses several other economic benefits. Since the design procedure can be standardized and automatized, it can reduce the design cost itself and the skill acquirement periods required for new or inapt design crews. Further, due to the standardized and automatized design procedure, any telecommunication network design results can accessed more objectively.

다중 임무점 방문을 위한 경로 계획의 복잡도는 단일 구간 경로 계획을 위한 복잡도보다 크게 더 높다. n개의 다중 임무점을 방문하는 경로 계획을 위해서는 $n^2+n$번의 단일 구간 경로 계획이 필요하다. 본 논문에서는 동적 변화 환경에서 다중 임무점을 방문하기 위한 최적의 경로 계획 알고리즘인 Multiple Mission $D^*$ Lite($MMD^*L$) 알고리즘을 제안하였다. $MMD^*L$은 앞서 수행된 단일 구간 경로 계획 정보를 재사용함으로써 복잡도를 감소시킨다. 시뮬레이션 결과를 통해 경로의 최적성은 양보하지 않으면서도 복잡도가 급격하게 감소하였음을 확인하였다.

The complexity of path planning for visiting multiple mission points is even larger than that of single pair path planning. Deciding a path for visiting n mission points requires conducting $n^2+n$ times of single pair path planning. We propose Multiple Mission $D^*$ Lite($MMD^*L$) which is an optimal path planning algorithm for visiting multiple mission points in dynamic environments. $MMD^*L$ reduces the complexity by reusing the computational data of preceding single pair path planning. Simulation results show that the complexity reduction is significant while its path optimality is not compromised.

본 논문은 AGV를 사용하는 물류자동화 공장에서 운송 시간을 단축하기 위해 최단 다 경로 알고리즘을 제안한다. 최단 다 경로 알고리즘은 A* 알고리즘을 보완한 것으로써 경로 분기점에서 최단 거리 선택을 위한 휴리스틱 함수($\hat{h}$(n))보다 작은 모든 간을 선택하여 저장하고 이를 재 탐색함으로써 다수의 경로를 생성한다. 생성된 경로를 본 논문에서 제안하는 소요시간 산출 방법을 이용하여 예상 소요 시간을 산출함으로써 최단시간에 목적지에 도달 가능한 최적 경로를 선택할 수 있음을 증명한다.

This study proposes an optimal path planning model for pedestrians and wheelchair users in convention centers, incorporating travel time, congestion, and corridor width?unlike prior research focused on shortest-path navigation?to provide the shortest-time path and the comfortable path. A case study reveals that wheelchair users experience longer travel times due to elevator waiting times and limited corridor width, while the occasional overlap of the two path types underscores the need for alternative routes for inclusive navigation, thereby extending path planning research to include wheelchair users and offering practical insights for customized guidance services and spatial management in convention centers.

This paper proposes a new algorithm for path planning and obstacles avoidance using the ant colony optimization algorithm. The proposed algorithm is a new hybrid algorithm that composes of the ant colony algorithm method and the Maklink graph method. At first, we produce the path of a mobile robot a the static environment. And then we find midpoints of each path using the Maklink graph. Finally the ant colony optimization algorithm is adopted to get a shortest path. In this paper, we prove the performance of the proposed algorithm is better than that of the Dijkstra algorithm through simulation.

In this paper, we applies two methods - Genetic Algorithm(GA) and $A^*$ Algorithm - to find the optimal path in route guidance system. Under the assumption that the traveling costs of each link are given, the task to find the optimal path becomes very complicated problem if the number of nodes or links increase. Two well-known algorithms are modified to resolve the problem and the preliminary demonstration show both optimistic result and needs to improvement.

본 논문에서는 노드의 위치 정보와 유효성 경로에 따라 클러스터링내의 헤드 노드를 선출하는 방법 중 하나로 에너지 효율성을 고려한 ECOPS(Energy Conserving Optimal path Schedule) 알고리즘을 제안한다. 기존 LEACH 알고리즘은 헤드 노드를 선출할 때 노드의 에너지 확률적 분포 함수에 기반 하여 헤드 노드의 주기를 선택적으로 관리하게 된다. 그러나 이 경우 중계노드의 거리 정보 등 상황 정보 인자가 반영되지 않아 위치적으로 또는 중계노드로 적당하지 않은 노드들이 확률분포에 포함되어 헤드노드로 선택 되는 경우가 발생한다. 따라서 본 논문에서는 기존의 LEACH 기반에서 계층적인 클러스터 구조의 토폴로지로부터 헤드 노드를 선택함에 있어 인접한 노드와의 위치상황 정보인자 및 잔존에너지의 상황정보를 이용하는 ECOPS 알고리즘을 제안 한다. 제안된 ECOPS 알고리즘은 헤드 노드 교체 상황에서 후보 헤드노드 중 최적의 효율적인 에너지 보존 경로를 가지는 멤버 노드가 새로운 헤드 노드로 선출됨으로써 전체 노드 수명 및 네트워크의 관리를 향상시키는 것으로 모의실험 결과를 나타내었다.

We are propose the position of the node context-awareness information and the validity of the head node in the path according to the clustering how to elect one of the energy efficiency ECOPS (Energy Conserving Optimal path Schedule) algorithm. Existing LEACH algorithm to elect the head node when the node's energy probability distribution function based on the management of the head node is optional cycle. However, in this case, the distance of the relay node status information including context-awareness parameters does not reflect. These factors are not suitable for the relay node or nodes are included in the probability distribution, if the head node selects occurs. In particular, to solve the problems from the LEACH-based hierarchical clustering algorithms, this study defines location with the status context information and the residual energy factor in choosing topology of the structure adjacent nodes. ECOPS algorithm that contextual information is contributed for head node selection in topology protocols. The proposed ECOPS algorithm has the head node replacement situations from the candidate head node in the optimal path and efficient energy conservation that is the path of the member nodes. The new head node election show as the entire node lifetime and network management technique improving the network lifetime and efficient management the simulation results.