• Title, Summary, Keyword: Ant Colony Optimization(ACO)

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Satellite Customer Assignment: A Comparative Study of Genetic Algorithm and Ant Colony Optimization

  • Kim, Sung-Soo;Kim, Hyoung-Joong;Mani, V.
    • Journal of Ubiquitous Convergence Technology
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    • v.2 no.1
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    • pp.40-50
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    • 2008
  • The problem of assigning customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. For this combinatorial optimization problem, standard optimization methods take a large computation time and so genetic algorithms (GA) and ant colony optimization (ACO) can be used to obtain the best and/or optimal assignment of customers to satellite channels. In this paper, we present a comparative study of GA and ACO to this problem. Various issues related to genetic algorithms approach to this problem, such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. We also discuss an ACO for this problem. In ACO methodology, three strategies, ACO with only ranking, ACO with only max-min ant system (MMAS), and ACO with both ranking and MMAS, are considered. A comparison of these two approaches (i,e., GA and ACO) with the standard optimization method is presented to show the advantages of these approaches in terms of computation time.

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An Ant Colony Optimization Approach for the Maximum Independent Set Problem (개미 군집 최적화 기법을 활용한 최대 독립 마디 문제에 관한 해법)

  • Choi, Hwayong;Ahn, Namsu;Park, Sungsoo
    • Journal of Korean Institute of Industrial Engineers
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    • v.33 no.4
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    • pp.447-456
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    • 2007
  • The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

A Hybrid Genetic Ant Colony Optimization Algorithm with an Embedded Cloud Model for Continuous Optimization

  • Wang, Peng;Bai, Jiyun;Meng, Jun
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1169-1182
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    • 2020
  • The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACOR, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

An Ant Colony Optimization Approach for the Two Disjoint Paths Problem with Dual Link Cost Structure

  • Jeong, Ji-Bok;Seo, Yong-Won
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.308-311
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    • 2008
  • The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

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A Novel Hybrid Intelligence Algorithm for Solving Combinatorial Optimization Problems

  • Deng, Wu;Chen, Han;Li, He
    • Journal of Computing Science and Engineering
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    • v.8 no.4
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    • pp.199-206
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    • 2014
  • The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

A Novel Binary Ant Colony Optimization: Application to the Unit Commitment Problem of Power Systems

  • Jang, Se-Hwan;Roh, Jae-Hyung;Kim, Wook;Sherpa, Tenzi;Kim, Jin-Ho;Park, Jong-Bae
    • Journal of Electrical Engineering and Technology
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    • v.6 no.2
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    • pp.174-181
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    • 2011
  • This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

A hybrid imperialist competitive ant colony algorithm for optimum geometry design of frame structures

  • Sheikhi, Mojtaba;Ghoddosian, Ali
    • Structural Engineering and Mechanics
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    • v.46 no.3
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    • pp.403-416
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    • 2013
  • This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

NoC-Based SoC Test Scheduling Using Ant Colony Optimization

  • Ahn, Jin-Ho;Kang, Sung-Ho
    • ETRI Journal
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    • v.30 no.1
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    • pp.129-140
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    • 2008
  • In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

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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
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    • v.35 no.10
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    • pp.135-142
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    • 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 Performance Evaluation of the Variations of Ant Colony Optimization for Vehicle Routing Problems with Time Windows (시간대 제약이 있는 차량경로문제를 위한 Ant Colony Optimization의 변형들의 성능평가)

  • Hong, Sung-Chul;Park, Yang-Byung
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.319-322
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    • 2004
  • 물류/택배업계의 공급사슬관리에서 차량에 의한 고객의 요구 서비스 시간대 만족은 고객의 재고수준을 낮추고 또한 서비스 수준의 향상에 매우 중요한 제약조건이다. 최근에 소개된 메타휴리스틱인 개미해법(Ant Colony Optimization: ACO)은 NP-hard 문제의 해공간 탐색에 있어서 상당한 장점을 가지고 있으나, 시간대 제약이 있는 차량경로문제(Vehicle Routing Problems with Time Windows: VRPTW)에 대한 적용은 아주 미비한 실정이다. 따라서, 본 연구에서는 ACO 를 VRPTW에 적용하여 최선의 차량경로 해를 구하기 위한 여러 변형을 제시하고, 이들의 영향을 다양한 실험문제를 이용하여 분석하고자 한다. 계산실험 결과, 기본 ACO 에 여러 설계 요소들을 추가함에 따라 계산시간이 다소 증가하지만 보다 우수한 차량경로 해를 구할 수 있었다.

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