• Title/Summary/Keyword: Ant Colony Optimization

Search Result 140, Processing Time 0.026 seconds

Optimal solution search method by using modified local updating rule in Ant Colony System (개미군락시스템에서 수정된 지역 갱신 규칙을 이용한 최적해 탐색 기법)

  • Hong, Seok-Mi;Chung, Tae-Choong
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.14 no.1
    • /
    • pp.15-19
    • /
    • 2004
  • 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 number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

Ant Colony Algorithm based Optimization Methodology for Product Family Redesign (Ant Colony 알고리즘 기반의 Product Family 재설계를 위한 최적화 방법론)

  • Seo, Kwang-Kyu
    • Journal of the Korea Safety Management & Science
    • /
    • v.13 no.1
    • /
    • pp.175-182
    • /
    • 2011
  • 고객의 요구에 대한 빠른 대응과 유연하고 효율적으로 새로운 제품을 적기에 개발하기 위해서는 제품 플랫폼에 기초한 대량 맞춤이 절실히 요구된다. 이러한 목적을 달성하기 위하여 기업들은 상대적으로 생산비용을 낮게 유지하면서 대량생산의 이점을 유지하고 동시에 고객의 요구사항을 만족시키기 위해, product family를 도입하고 가능하면 작은 변화를 통하여 제품의 다양성을 유지하고자 한다. Product family를 설계할 때 중요한 이슈 중에 하나는 제품의 공통성과 차별성간의 절충점을 찾아내는 것인데, 본 연구에서는 설계자들이 product family 재설계를 용이하게 하기 위한 방법론을 제안한다. 이를 위하여 본 연구에서는 ant colony 알고리즘과 product family의 공통성 평가지수를 이용하여 product family 재설계 방법론을 개발한다. 제안한 방법론은 복잡하고 반복적인 많은 계산과정을 가지고 있는 다른 방법과 달리 메타 휴리스틱 알고리즘을 적용하여 인간의 간섭을 줄이고, 실험결과의 정확도, 반복성 및 강건성을 향상시킨다. 본 연구에서는 컴퓨터 마우스 제품군을 대상으로 제안한 방법의 타당성을 검증하였고, 추가적으로 product family 레벨과 부품 레벨의 product family 재설계 추천방안도 제시하였다.

A Low Power ECC H-matrix Optimization Method using an Ant Colony Optimization (ACO를 이용한 저전력 ECC H-매트릭스 최적화 방안)

  • Lee, Dae-Yeal;Yang, Myung-Hoon;Kim, Yong-Joon;Park, Young-Kyu;Yoon, Hyun-Jun;Kang, Sung-Ho
    • Journal of the Institute of Electronics Engineers of Korea SD
    • /
    • v.45 no.1
    • /
    • pp.43-49
    • /
    • 2008
  • In this paper, a method using the Ant Colony Optimization(ACO) is proposed for reducing the power consumption of memory ECC checker circuitry which provide Single-Error Correcting and Double-Error Detecting(SEC-DED). The H-matrix which is used to generate SEC-DED codes is optimized to provide the minimum switching activity with little to no impact on area or delay using the symmetric property and degrees of freedom in constructing H-matrix of Hsiao codes. Experiments demonstrate that the proposed method can provide further reduction of power consumption compared with the previous works.

DEVELOPMENT OF AUTONOMOUS QoS BASED MULTICAST COMMUNICATION SYSTEM IN MANETS

  • Sarangi, Sanjaya Kumar;Panda, Mrutyunjaya
    • International Journal of Computer Science & Network Security
    • /
    • v.21 no.8
    • /
    • pp.342-352
    • /
    • 2021
  • Multicast Routings is a big challenge due to limitations such as node power and bandwidth Mobile Ad-hoc Network (MANET). The path to be chosen from the source to the destination node requires protocols. Multicast protocols support group-oriented operations in a bandwidth-efficient way. While several protocols for multi-cast MANETs have been evolved, security remains a challenging problem. Consequently, MANET is required for high quality of service measures (QoS) such infrastructure and application to be identified. The goal of a MANETs QoS-aware protocol is to discover more optimal pathways between the network source/destination nodes and hence the QoS demands. It works by employing the optimization method to pick the route path with the emphasis on several QoS metrics. In this paper safe routing is guaranteed using the Secured Multicast Routing offered in MANET by utilizing the Ant Colony Optimization (ACO) technique to integrate the QOS-conscious route setup into the route selection. This implies that only the data transmission may select the way to meet the QoS limitations from source to destination. Furthermore, the track reliability is considered when selecting the best path between the source and destination nodes. For the optimization of the best path and its performance, the optimized algorithm called the micro artificial bee colony approach is chosen about the probabilistic ant routing technique.

Using Ant Colony Optimization to Find the Best Precautionary Measures Framework for Controlling COVID-19 Pandemic in Saudi Arabia

  • Alshamrani, Raghad;Alharbi, Manal H.
    • International Journal of Computer Science & Network Security
    • /
    • v.22 no.10
    • /
    • pp.352-358
    • /
    • 2022
  • In this paper, we study the relationship between infection rates of covid 19 and the precautionary measures and strict protocols taken by Saudi Arabia to combat the spread of the coronavirus disease and minimize the number of infected people. Based on the infection rates and the timetable of precautionary measures, the best framework of precautionary measures was identified by applying the traveling salesman problem (TSP) that relies on ant colony optimization (ACO) algorithms. The proposed algorithm was applied to daily infected cases data in Saudi Arabia during three periods of precautionary measures: partial curfew, whole curfew, and gatherings penalties. The results showed the partial curfew and the whole curfew for some cities have the minimum total cases over other precautionary measures. The gatherings penalties had no real effect in reducing infected cases as the other two precautionary measures. Therefore, in future similar circumstances, we recommend first applying the partial curfew and the whole curfew for some cities, and not considering the gatherings penalties as an effective precautionary measure. We also recommend re-study the application of the grouping penalty, to identify the reasons behind the lack of its effectiveness in reducing the number of infected cases.

Truss Size Optimization with Frequency Constraints using ACO Algorithm (개미군락 최적화 알고리즘을 이용한 진동수 구속조건을 가진 트러스구조물의 크기최적화)

  • Lee, Sang-Jin;Bae, Jungeun
    • Journal of the Architectural Institute of Korea Structure & Construction
    • /
    • v.35 no.10
    • /
    • pp.135-142
    • /
    • 2019
  • Ant colony optimization(ACO) technique is utilized in truss size optimization with frequency constraints. Total weight of truss to be minimized is considered as the objective function and multiple natural frequencies are adopted as constraints. The modified traveling salesman problem(TSP) is adopted and total length of the TSP tour is interpreted as the weight of the structure. The present ACO-based design optimization procedure uses discrete design variables and the penalty function is introduced to enforce design constraints during optimization process. Three numerical examples are carried out to verify the capability of ACO in truss optimization with frequency constraints. From numerical results, the present ACO is a very effective way of finding optimum design of truss structures in free vibration. Finally, we provide the present numerical results as future reference solutions.

A Effective Ant Colony Algorithm applied to the Graph Coloring Problem (그래프 착색 문제에 적용된 효과적인 Ant Colony Algorithm에 관한 연구)

  • Ahn, Sang-Huck;Lee, Seung-Gwan;Chung, Tae-Choong
    • The KIPS Transactions:PartB
    • /
    • v.11B no.2
    • /
    • pp.221-226
    • /
    • 2004
  • Ant Colony System(ACS) Algorithm is new meta-heuristic for hard combinational optimization problem. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. Recently, various methods and solutions are proposed to solve optimal solution of graph coloring problem that assign to color for adjacency node($v_i, v_j$) that they has not same color. In this paper introducing ANTCOL Algorithm that is method to solve solution by Ant Colony System algorithm that is not method that it is known well as solution of existent graph coloring problem. After introducing ACS algorithm and Assignment Type Problem, show the wav how to apply ACS to solve ATP And compare graph coloring result and execution time when use existent generating functions(ANT_Random, ANT_LF, ANT_SL, ANT_DSATUR, ANT_RLF method) with ANT_XRLF method that use XRLF that apply Randomize to RLF to solve ANTCOL. Also compare graph coloring result and execution time when use method to add re-search to ANT_XRLF(ANT_XRLF_R) with existent generating functions.

Application Core Mapping to Minimize the Network Latency on Regular NoC Architectures (규칙적인 NoC 구조에서의 네트워크 지연 시간 최소화를 위한 어플리케이션 코어 매핑 방법 연구)

  • Ahn, Jin-Ho;Kim, Hong-Sik;Kim, Hyun-Jin;Park, Young-Ho;Kang, Sung-Ho
    • Journal of the Institute of Electronics Engineers of Korea SD
    • /
    • v.45 no.4
    • /
    • pp.117-123
    • /
    • 2008
  • In this paper, we propose a novel ant colony optimization(ACO)-based application core ma ins method for implementing network-on-chip(NoC)-based systems-on-chip(SoCs). The proposed method efficiently put application cores to a mesh-type NoC satisfying a given design objective, the network latency. Experimental results using a functional circuit including 12 cores show that the proposed algorithm can produce near optimal mapping results within a second.

A Common Bitmap Block Truncation Coding for Color Images Based on Binary Ant Colony Optimization

  • Li, Zhihong;Jin, Qiang;Chang, Chin-Chen;Liu, Li;Wang, Anhong
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.10 no.5
    • /
    • pp.2326-2345
    • /
    • 2016
  • For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding (BTC) of the R, G and B channels. However, common bitmaps generated by some traditional schemes are not the best possible because they do not consider the minimized distortion of the entire color image. In this paper, we propose a near-optimized common bitmap scheme for BTC using Binary Ant Colony Optimization (BACO), producing a BACO-BTC scheme. First, the color image is compressed by the BTC algorithm to get three individual bitmaps, and three pairs of quantization values for the R, G, and B channels. Second, a near-optimized common bitmap is generated with minimized distortion of the entire color image based on the idea of BACO. Finally, the color image is reconstructed easily by the corresponding quantization values according to the common bitmap. The experimental results confirmed that reconstructed image of the proposed scheme has better visual quality and less computational complexity than the referenced schemes.

Centroid and Nearest Neighbor based Class Imbalance Reduction with Relevant Feature Selection using Ant Colony Optimization for Software Defect Prediction

  • B., Kiran Kumar;Gyani, Jayadev;Y., Bhavani;P., Ganesh Reddy;T, Nagasai Anjani Kumar
    • International Journal of Computer Science & Network Security
    • /
    • v.22 no.10
    • /
    • pp.1-10
    • /
    • 2022
  • Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.