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Clarifying Cutting and Sewing Processes with Due Windows Using an Effective ant Colony Optimization

MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-12页

作者： Huang, Rong-Hwa Yu, Shun-Chi Fu Jen Catholic Univ Dept Business Adm New Taipei City Taiwan Fu Jen Catholic Univ Grad Sch Business Adm New Taipei City Taiwan

The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO) algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

关键词： ant algorithms PRODUCTION scheduling COMPUTATIONAL complexity ROBUST control PARTICLE swarm optimization PROBLEM solving

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Modifications of the ant colony method for aviation routing problems

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AUTOMATION AND REMOTE CONTROL 2015年第3期76卷 458-471页

作者： Titov, Yu. P. Moscow Inst Aviat Technol Moscow 125871 Russia

We study the ant colony method developed to solve the traveling salesman problem. We consider the possibility to apply the ant colony method to other graph-based problems: to the problem of collecting resources under various constraints and conditions and to the routing problem for several vehicles with the possibility to choose the place where these vehicles are located. We develop an algorithm that implements this method. We give an estimate of the algorithm's efficiency for various problems. The algorithms has proven to converge quickly, and the resulting solution is close to the optimal. This method can be recommended for solving most graph-based problems.

关键词： ant algorithms MATHEMATICAL optimization AUTOMATIC control GRAPH algorithms METAHEURISTIC algorithms

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Detection of defective pile geometries using a coupled FEM/SBFEM approach and an ant colony classification algorithm

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ACTA MECHANICA 2016年第5期227卷 1279-1291页

作者： Psychas, Iraklis Dimitrios Schauer, Marco Boehrnsen, Jens-Uwe Marinaki, Magdalene Marinakis, Yannis Langer, Sabine C. Stavroulakis, Georgios E. Tech Univ Crete Sch Prod Engn & Management Khania 73100 Greece Tech Univ Carolo Wilhelmina Braunschweig Inst Stat D-38106 Braunschweig Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Konstrukt Tech IK Arbeitsgrp Vibroakust D-38106 Braunschweig Germany

Piles are widely used to build a proper foundation for various buildings. The piles' quality in situ can be tested by a so-called pile integrity test. In order to apply this test, an impulse is given to the piles' head which generates a p-wave running through the pile. An acceleration sensor is attached to the piles' head, to measure the vertical movement. The major part of this wave is reflected from the piles' toe and is measured by the attached acceleration sensor on top of the pile. This yields an acceleration-time plot which has to be analysed in order to determine the piles' condition with respect to structural consistence and mostly radius defects. Since deviations in the cross section of the pile cause additional reflections, suitable post-processing can be used in order to detect these defects. In this paper, we propose an ant colony classification model to detect structural defects in piles by evaluating displacement-time plots to improve the reliability of pile monitoring. The data of these plots result to numerically performed pile integrity tests. To conduct these tests, a simulation of a combined finite element method and scaled boundary finite element methods has been carried out. These results are used for learning and training the ant colony classification model and to have different sets of data to validate the optimization algorithm. The position and the type of piles' defect can be identified by the applied algorithm.

关键词： ant algorithms ACCELERATION (Mechanics) RELIABILITY (Engineering) FINITE element method MATHEMATICAL optimization

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Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第2期6卷 176-187页

作者： Abdelaziz, A. Y. Osama, R. A. El-Khodary, S. M. Ain Shams Univ Fac Engn Dept Elect Power & Machines Cairo Egypt

This study introduces the ant colony optimisation (ACO) algorithm implemented in the hyper-cube (HC) framework to solve the distribution network minimum loss reconfiguration problem. The ACO is a relatively new and powerful intelligence evolution method for solving optimisation problems. It is a population-based approach inspired from natural behaviour of real ant colonies. In contrast to the usual ways of implementing ACO algorithms, the HC framework limits the pheromone values by introducing changes in the pheromone updating rules resulting in a more robust and easier to implement version of the ACO procedure. The optimisation problem is formulated taking into account the operational constraints of the distribution systems. Results of numerical tests carried out on three test systems from literature are presented to show the effectiveness of the proposed approach.

关键词： ant algorithms MATHEMATICAL optimization ELECTRIC power distribution TEST systems PHEROMONES

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A convenient and robust edge detection method based on ant colony optimization

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OPTICS COMMUNICATIONS 2015年 353卷 147-157页

作者： Liu, Xiaochen Fang, Suping Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China

Edge detection is usually used as a preprocessing operation in many machine vision industrial applications. Recently, ant colony optimization (ACO) as a relatively new meta-heuristic approach has been used to tackle the edge detection problem. In this work, a convenient and robust method for edge detection based on ACO is proposed, which employs a new heuristic function, adopts a user-defined threshold in pheromone update process and provides a group of suitable parameter values. Experimental results clearly demonstrated the effectiveness of the proposed method, and at the same time, in the presence of noise, the proposed approach outperforms other two ACO-based edge detection techniques and four conventional edge detectors. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Edge detection ant colony optimization ant algorithms

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Complete Inverse Method Using ant Colony Optimization Algorithm for Structural Parameters and Excitation Identification from Output Only Measurements

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-18页

作者： Chen, Jun Chen, Xin Liu, Wei Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China

In vibration-based structural health monitoring of existing large civil structures, it is difficult, sometimes even impossible, to measure the actual excitation applied to structures. Therefore, an identification method using output-only measurements is crucial for the practical application of structural health monitoring. This paper integrates the ant colony optimization (ACO) algorithm into the framework of the complete inverse method to simultaneously identify unknown structural parameters and input time history using output-only measurements. The complete inverse method, which was previously suggested by the authors, converts physical or spatial information of the unknown input into the objective function of an optimization problem that can be solved by the ACO algorithm. ACO is a newly developed swarm computation method that has a very good performance in solving complex global continuous optimization problems. The principles and implementation procedure of the ACO algorithm are first introduced followed by an introduction of the framework of the complete inverse method. Construction of the objective function is then described in detail with an emphasis on the common situation wherein a limited number of actuators are installed on some key locations of the structure. Applicability and feasibility of the proposed method were validated by numerical examples and experimental results from a three-story building model.

关键词： ant algorithms STRUCTURAL health monitoring CIVIL engineering STRUCTURAL analysis (Engineering) VIBRATION (Mechanics) PARAMETERS (Statistics)

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Minimum Cost Multicast Routing Using ant Colony Optimization Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-13页

作者： Hu, Xiao-Min Zhang, Jun Sun Yat Sen Univ Dept Comp Sci Key Lab Intelligent Sensor Networks Minist EducKey Lab Software TechnolEduc Dept Gu Guangzhou 510006 Guangdong Peoples R China

Multicast routing (MR) is a technology for delivering network data from some source node(s) to a group of destination nodes. The objective of the minimum cost MR (MCMR) problem is to find an optimal multicast tree with the minimum cost for MR. This problem is NP complete. In order to tackle the problem, this paper proposes a novel algorithm termed the minimum cost multicast routing ant colony optimization (MCMRACO). Based on the ant colony optimization (ACO) framework, the artificial ants in the proposed algorithm use a probabilistic greedy realization of Prim's algorithm to construct multicast trees. Moving in a cost complete graph (CCG) of the network topology, the ants build solutions according to the heuristic and pheromone information. The heuristic information represents problem-specific knowledge for the ants to construct solutions. The pheromone update mechanisms coordinate the ants' activities by modulating the pheromones. The algorithm can quickly respond to the changes of multicast nodes in a dynamic MR environment. The performance of the proposed algorithm has been compared with published results available in the literature. Results show that the proposed algorithm performs well in both static and dynamic MCMR problems.

关键词： ant algorithms MULTICASTING (Computer networks) ROUTING (Computer network management) MATHEMATICAL optimization HEURISTIC algorithms GRAPH theory

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KantS: A Stigmergic ant Algorithm for Cluster Analysis and Swarm Art

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第6期44卷 843-856页

作者： Fernandes, Carlos M. Mora, antonio M. Merelo, Juan J. Rosa, Agostinho C. Univ Tecn Lisboa Lab Robot & Syst Engn P-1049001 Lisbon Portugal Univ Granada Dept Comp Technol & Architecture E-18071 Granada Spain

KantS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KantS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KantS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KantS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.

关键词： ant algorithms cluster analysis self-organization stigmergy swarm art swarm intelligence

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Road Network Vulnerability Analysis Based on Improved ant Colony Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-9页

作者： Wang, Yunpeng Feng, Yuqin Li, Wenxiang Fulcher, William Case Zhang, Li Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Heilongjiang Inst Technol Sch Automobile Engn Harbin 150050 Peoples R China Tongji Univ Minist Educ Key Lab Rd Traff Engn Shanghai 201804 Peoples R China Mississippi State Univ Dept Civil & Environm Engn Starkville MS 39759 USA

We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

关键词： ant algorithms TRAFFIC flow VULNERABILITY (Psychology) TRANSPORTATION planning TRANSPORTATION management system

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ant colony recognition systems for part clustering problems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第15期46卷 4237-4258页

作者： Kao, Y. Li, Y. L. Tatung Univ Dept Informat Management Taipei 104 Taiwan

Cellular manufacturing requires an effective part clustering method to start up the manufacturing cell design. This paper presents a new part clustering algorithm that uses the concept of the recognition system of artificial ants. The proposed algorithm mimics the random meetings of real ants to build up the ability of object recognition and then to form many initial part clusters with high similarities. These initial part clusters are further merged into larger and larger clusters in an agglomerative way until the designated number of part families is reached. The characteristics of artificial ants, such as randomization and collective behaviour, allow the algorithm to re-cluster wrongly grouped parts into the proper clusters. As a result this can eliminate the chaining effects resulting from the interference of abnormal parts during the clustering process. This algorithm has been developed into a software system called the ant colony recognition system (ACRS). A number of problems selected from the literature have been solved by ACRS, and the evaluation results indicate that ACRS is able to solve the cell formation problems effectively.

关键词： ant algorithms cluster analysis cell formation group technology

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