• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.5
• /
• pp.939-955
• /
• 2010

With the progress of IT and mobile positioning technologies, various types of location-based services (LBS) have been proposed and implemented. Finding a shortest path between two nodes is one of the most fundamental tasks in many LBS related applications. So far, there have been many research efforts on the shortest path finding problem. For instance, $A^*$ algorithm estimates neighboring nodes using a heuristic function and selects minimum cost node as the closest one to the destination. Pruning method, which is known to outperform the A* algorithm, improves its routing performance by avoiding unnecessary exploration in the search space. For pruning, shortest paths for all node pairs in a map need to be pre-computed, from which a shortest path container is generated for each edge. The container for an edge consists of all the destination nodes whose shortest path passes through the edge and possibly some unnecessary nodes. These containers are used during routing to prune unnecessary node visits. However, this method shows poor performance as the number of unnecessary nodes included in the container increases. In this paper, we focus on this problem and propose a new border line-based pruning scheme for path routing which can reduce the number of unnecessary node visits significantly. Through extensive experiments on randomly-generated, various complexity of maps, we empirically find out optimal number of border lines for clipping containers and compare its performance with other methods.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.3
• /
• pp.147-152
• /
• 2008

Ant System(AS) is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, AS is applied to the Multicast Routing Problem. Multicast Routing is modeled as the NP-complete Steiner tree problem. This is the shortest path from source node to all destination nodes. We proposed new AS to resolve this problem. The proposed method selects the neighborhood node to consider all costs of the edge and the next node in state transition rule. Also, The edges which are selected elite agents are updated to additional pheromone. Simulation results of our proposed method show fast convergence and give lower total cost than original AS and $AS_{elite}$.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.5
• /
• pp.19-27
• /
• 2007

We have implemented a WWW homepage which finds an optimal route for users. There already exist many web sites which provide the optimal route when a start and a destination cities are given. However, none of them can find the optimal route when a number of cities to be visited. The problem of finding the optimal route starting at a given start city and visiting through all the given intermediate cities and finally returning to the start city is called Travelling Sales Person(TSP) problem. TSP is a well known exponential time complexity problem. We have implemented an artificial intelligent search algorithm for TSP on our homepage. The main feature of our algorithm is that the destination may not be the same as the start city whereas all of the existing heuristic algorithms for TSP assume that the start and the destination cities are the same. The web page asks a user to select all the cities he or she wants to visit(including start and destination city), then it finds a sequence of the cities such that the user would travel minimum distance if he or she visits the cities in the order of the sequence. This paper presents algorithms used in the homepage.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.3
• /
• pp.203-210
• /
• 2011

Ant Colony System is a new meta heuristics algorithms to solve hard combinatorial optimization problems. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the searching method to consider the overlapping edge of the global best path of the previous and the current. This method is that we first determine the overlapping edge of the global best path of the previous and the current will be configured likely the optimal path. And, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath and ACS-Iter algorithms.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.2
• /
• pp.179-190
• /
• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• Journal of Intelligence and Information Systems
• /
• v.3 no.2
• /
• pp.1-9
• /
• 1997

These papers propose an evolutionary algorithm for re-using output of waste fuel of light water reactor system in nuclear power plants. Evolutionary algorithm is useful for optimization of the large space problem. The wastes contain several re-useable elements, and they should be carefully selected and blended to satisfy requirements as input material to the heavy water nuclear reactor system. This problem belongs to a NP-hard like the 0/1 Knapsack problem. Two evolutionary strategies are used as a, pp.oximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method, which performs the feasible teat and solution evaluation by using the vectorized data in problem. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• Journal of Intelligence and Information Systems
• /
• v.13 no.4
• /
• pp.121-135
• /
• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Korean Journal of Remote Sensing
• /
• v.18 no.1
• /
• pp.1-12
• /
• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.303-313
• /
• 2019

After a disaster happens in urban areas, many people need support for a quick evacuation. This work aims to develop a method for the calculation of the most feasible evacuation route inside buildings. In the methodology we simplify the geometry of the structural and non structural elements from the BIM (Building Information Modeling) to store them in a spatial database which follows standards to support vector data. Then, we apply the multicriteria analysis with the allocation of prioritization values and weight factors validated through the AHP (Analytic Hierarchy Process), in order to obtain the Importance Index S(n) of the elements. The criteria consider security conditions and distribution of the building's facilities. The S(n) is included as additional heuristic data for the calculation of the evacuation route through an algorithm developed as a variant of the $A^*$ pathfinding, The experimental results in the simulation of evacuation scenarios for vulnerable people in healthy physical conditions and for the elderly group, shown that the conditions about the wide of routes, restricted areas, vulnerable elements, floor roughness and location of facilities in the building applied in the multicriteria analysis has a high influence on the processing of the developed variant of $A^*$ algorithm. The criteria modify the evacuation route, because they considers as the most feasible route, the safest instead of the shortest, for the simulation of evacuation scenarios for people in healthy physical conditions. Likewise, they consider the route with the location of facilities for the movement of the elderly like the most feasible in the simulation of evacuation route for the transit of the elderly group. These results are important for the assessment of the decision makers to select between the shortest or safest route like the feasible for search and rescue activities.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3834-3857
• /
• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.