• Journal of Digital Convergence
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• v.11 no.11
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• pp.443-448
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• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

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

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.709-712
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• 2010

본 논문은 개미군락최적화 알고리즘을 이용한 트러스 구조물의 설계최적화에 대한 이론적 배경과 수치해석 결과를 기술하였다. 트러스의 설계최적화를 수행하기 위하여 구조물의 중량을 최소화하는 것을 목적 함수로 하고 구조물에서 발생하는 응력과 변위의 허용치를 초과하지 않는 것을 구속조건으로 이용하였다. 본 연구에서는 개미군락알고리즘을 구조물의 최적화에 적용하기 위하여 외판원문제(travelling salesman problem: TSP)를 재 정의하는 방법을 사용하였으며 최대-최소개미시스템(max-min ant system)을 도입하여 트러스 구조물의 최적설계를 수행하였다. 이때 이산화 된 설계변수를 사용하였으며 구속조건을 처리하기 위해서 벌점함수를 사용하였다. 본 연구를 통하여 개미군락최적화 알고리즘은 구조최적화에 그 적용 가능성이 높았으며 전통적인 최적검색 기법의 새로운 대안으로 이용될 수 있는 것으로 나타났다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.346-353
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• 2011

Routing schemes that service applications with various delay times, maintaining the long network life time are required in wireless sensor & actor networks. However, it is known that network lifetime and hop count of trees used in routing methods have the tradeoff between them. In this paper, we propose a Pareto Ant Colony Optimization algorithm to find the Pareto tree set such that it optimizes these both tradeoff objectives. As it enables applications which have different delay times to select appropriate routing trees, not only satisfies the requirements of various multiple applications but also guarantees long network lifetime. We show that the Pareto tree set found by proposed algorithm consists of trees that are closer to the Pareto optimal points in terms of hop count and network lifetime than minimum spanning tree which is a representative routing tree.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.191-198
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• 2013

It is required to transmit data through shorter path between sensor and base node for real time intrusion detection in wireless sensor networks (WSN) with a mobile base node. Because minimum Wiener index spanning tree (MWST) based routing approach guarantees lower average hop count than that of minimum spanning tree (MST) based routing method in WSN, it is known that MWST based routing is appropriate for real time intrusion detection. However, the minimum Wiener index spanning tree problem which aims to find a spanning tree which has the minimum Wiener index from a given weighted graph was proved to be a NP-hard. And owing to its high dependency on certain nodes, minimum Wiener index tree based routing method has a shorter network lifetime than that of minimum spanning tree based routing method. In this paper, we propose a multi-objective ant colony optimization algorithm to tackle these problems, so that it can be used to detect intrusion in real time in wireless sensor networks with a mobile base node. And we compare the results of our proposed method with MST based routing and MWST based routing in respect to average hop count, network energy consumption and network lifetime by simulation.

• Journal of Advanced Navigation Technology
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• v.18 no.1
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• pp.44-49
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• 2014

In recent, watermarking technology have been paying attention to methods avoiding illegal use and reproduce of digital contents. Then, in order to protect the right of digital contents, a watermark image is inserted into original images. In different watermarking methods, several technologies using Ant Colony Algorithm have been studied. In this paper, we propose a watermarking method using a discrete optimization method in the ants colony algorithm. This proposed method resembls the process that ants follow the pheromone traps to find out food. And when a watermark image is inserted into original images, the proposed method considers the deployment of obstacles or the balance between cells in the entire digital image. Simulation results show that the proposed method is increased in robustness of watermarked image and is decreased in the perceptibility of watermarking compared to the previous methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.3
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• pp.441-453
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• 2017

In this paper, a new type of weapon-target assignment(WTA) problem has been suggested that reflects realistic constraints for sharing target with other weapons and shooting double rapid fire. To utilize in rapidly changing actual battle field, the computation time is of great importance. Several metaheuristic methods such as Simulated Annealing, Tabu Search, Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization have been applied to the real-time WTA in order to find a near optimal solution. A case study with a large number of targets in consideration of the practical cases has been analyzed by the objective value of each algorithm.

• Advances in Computational Design
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• v.2 no.4
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• pp.313-331
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• 2017

In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.596-604
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• 2020

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.