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ant colony optimization algorithm to the inter-cell layout problem in cellular manufacturing

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2004年第3期157卷 592-606页

作者： Solimanpur, M Vrat, P Shankar, R Indian Inst Technol Dept Management Studies New Delhi 110016 India Indian Inst Technol Roorkee 247667 Uttar Pradesh India Indian Inst Technol Dept Engn Mech New Delhi 110016 India

The inter-cell layout problem is discussed and a mathematical formulation for material flow between the cells is presented. The problem is modeled as a quadratic assignment problem (QAP). An ant algorithm is developed to solve the formulated problem. The performance of the proposed ant algorithm is compared to the facility layout algorithms such as H63, HC63-66, CRAFT and Bubble Search as well as other existing ant colony implementations for QAP such as Fant, HAS-QAP, MMAS-QAP(2-opt), and antS algorithms. The experimental results show that the proposed ant algorithm performs significantly better than the facility layout algorithms. Also, our experimental results reveal that the proposed ant algorithm is effective and efficient as compared to other existing ant algorithms. (C) 2003 Elsevier B.V. All rights reserved.

关键词： facilities planning and design cellular manufacturing inter-cell layout problem quadratic assignment problem ant algorithms

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A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic ant Colony Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-10页

作者： Zhang, Yudong Wang, Shuihua Ji, Genlin Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210046 Jiangsu Peoples R China

In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features;second, a rule-based model was chosen to fit the given dataset since it can present physical meaning;third, a genetic ant colony algorithm (GACA) was introduced;the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm-fitness-scaling chaotic GACA (FSCGACA), which was used to seek the optimal parameters of the rule-based model;and finally, the stratified K-fold cross-validation technique was used to enhance the generalization of the model. Simulation experiments of 1000 corporations' data collected from 2006 to 2009 demonstrated that the proposed model was effective. It selected the 5 most important factors as "net income to stock broker's equality," " quick ratio," "retained earnings to total assets," "stockholders' equity to total assets," and "financial expenses to sales." The total misclassification error of the proposed FSCGACA was only 7.9%, exceeding the results of genetic algorithm (GA), ant colony algorithm (ACA), and GACA. The average computation time of the model is 2.02 s.

关键词： BANKRUPTCY PREDICTION models GENETIC algorithms ant algorithms CHAOS theory STRATEGIC planning

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An ant Optimization Model for Unrelated Parallel Machine Scheduling with Energy Consumption and Total Tardiness

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-8页

作者： Liang, Peng Yang, Hai-dong Liu, Guo-sheng Guo, Jian-hua Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Guangdong Peoples R China XingFa Aluminum Holdings Ltd Foshan 528061 Peoples R China Guangdong Univ Technol Sch Mech Engn Guangzhou 510006 Guangdong Peoples R China

This research considers an unrelated parallel machine scheduling problem with energy consumption and total tardiness. This problem is compounded by two challenges: differences of unrelated parallel machines energy consumption and interaction between job assignments and machine state operations. To begin with, we establish a mathematical model for this problem. Then an ant optimization algorithm based on ATC heuristic rule (ATC-ACO) is presented. Furthermore, optimal parameters of proposed algorithm are defined via Taguchi methods for generating test data. Finally, comparative experiments indicate the proposed ATC-ACO algorithm has better performance on minimizing energy consumption as well as total tardiness and the modified ATC heuristic rule is more effectively on reducing energy consumption.

关键词： ant algorithms MATHEMATICAL optimization PARALLEL computers COMPUTER scheduling TARDINESS ENERGY consumption

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Nonlinear Predictive Control of Mass Moment Aerospace Vehicles Based on ant Colony Genetic Algorithm Optimization

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-10页

作者： Zhang, Xiaoyu Li, Peng Xu, Dexin Mao, Ben He, Kunpeng Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

Based on the mathematical model of the mass moment aerospace vehicles (MMAV), a coupled nonlinear dynamical system is established by rational simplification. The flight control system of MMAV is designed via utilizing nonlinear predictive control (NPC) approach. Aiming at the parameters of NPC is generally used the trial-and-error method to optimize and design, a novel kind ofNPCparameters optimization strategy based on ant colony genetic algorithm(ACGA) is proposed in this paper. Themethod for setting NPC parameters with ACA in which the routes of ants are optimized by the genetic algorithm(GA) is derived. And then, a detailed realized process of this method is also presented. Furthermore, this optimization algorithm of the NPC parameters is applied to the flight control system of MMAV. The simulation results show that the system not only meets the demands of timeresponse specifications but also has excellent robustness.

关键词： NONLINEAR control theory PREDICTIVE control ant algorithms COMBINATORIAL optimization NONLINEAR dynamical systems PARAMETER estimation

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ant colony optimization for resource-constrained project scheduling

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2002年第4期6卷 333-346页

作者： Merkle, D Middendorf, M Schmeck, H Univ Karlsruhe Inst Appl Informat & Formal Descript Methods D-76128 Karlsruhe Germany Catholic Univ Eichstatt Ingolstadt Comp Sci Grp D-85072 Eichstatt Germany

An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented. Several new features that are interesting for ACO in general are proposed and evaluated. In particular, the use of a combination of two pheromone evaluation methods by the ants to find new solutions, a change of the influence of the heuristic on the decisions of the ants during the run of the algorithm, and the option that an elitist ant forgets the best-found solution are studied. We tested the ACO algorithm on a set of large benchmark problems from the Project Scheduling Library. Compared to several other heuristics for the RCPSP, including genetic algorithms, simulated annealing, tabu search, and different sampling methods our algorithm performed best on average. For nearly one-third of all benchmark problems, which were not known to be solved optimally before, the algorithm was able to find new best solutions.

关键词： ant algorithms ant colony optimization metaheuristics project scheduling RCPSP summation evaluation

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Parameter Selection for ant Colony Algorithm Based on Bacterial Foraging Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-12页

作者： Li, Peng Zhu, Hua China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China

The optimal performance of the ant colony algorithm(ACA) mainly depends on suitable parameters;therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

关键词： ant algorithms PARAMETERS (Statistics) STOCHASTIC convergence PARTICLE swarm optimization GENETIC algorithms COMPUTER simulation

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Research of ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-10页

作者： Li, Linhui Huang, Haiyang Lian, Jing Yao, Baozhen Zhou, Yafu Chang, Jing Zheng, Ning'an Dalian Univ Technol Fac Vehicle Engn & Mech Sch Automot Engn State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy's energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters "charge and discharge equivalent factors" for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

关键词： ADAPTIVE control systems ant algorithms HYBRID electric vehicles DYNAMICAL systems FUZZY algorithms

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Generalized ant Colony Optimizer: swarm-based meta-heuristic algorithm for cloud services execution

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COMPUTING 2019年第11期101卷 1609-1632页

作者： Kumar, Ajay Bawa, Seema Thapar Inst Engn & Technol Patiala Punjab India

This work presents a swarm-based meta-heuristic technique known as Generalized ant Colony Optimizer (GACO). It is a hybrid approach which consists of Simple ant Colony Optimization and Global Colony Optimization concepts. The main concept behind GACO is the foraging behavior of ants. GACO operates in the following four phases: Creation of a new colony, search of nearest food location, balance the solution, and updating of pheromone. GACO has been tested on seventeen well recognized standard benchmark functions and its results have been compared with three different meta-heuristic algorithms namely as Genetic Algorithm, Particle Swarm Optimization and Artificial Bee Colony. The performance metrics such as average and standard deviation are computed and evaluated with respect to these metrics. The proposed GACO performs better in comparison to the aforementioned algorithms. The proposed algorithm optimizes the cloud resource allocation problem and gives better results with unknown search spaces.

关键词： ant algorithms Meta-heuristics Cloud computing Optimization

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A parallel ant colony algorithm for bus network optimization

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2007年第1期22卷 44-55页

作者： Yang, Zhongzhen Yu, Bin Cheng, Chuntian Dalian Maritime Univ Transportat Coll Dalian 116026 Peoples R China Dalian Univ Technol Dept Civil Engn Dalian Peoples R China

This study presents an optimization model for a bus network design based on the coarse-grain parallel ant colony algorithm (CPACA). It aims to maximize the number of direct travelers per unit length, that is, direct traveler density, subject to route length and nonlinear rate constraints (ratio of the length of a route to the shortest road distance between the origin and destination). CPACA is a new optimal algorithm that (1) develops a new strategy to update the increased pheromone, called ant-Weight, by which the path-searching activities of ants are adjusted based on the objective function, and (2) uses parallelization strategies of an ant colony algorithm (ACA) to improve the calculation time and the quality of the optimization. Data collected in Dalian City, China, are used to test the model and the algorithm. The results show that the optimized bus network has significantly reduced transfers and travel time. They also reveal that the proposed CPACA is effective and efficient compared to some existing ant algorithms.

关键词： MATHEMATICAL optimization ant algorithms ant communities INSECT societies PHEROMONES MATHEMATICAL analysis

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The Improved ant Colony Optimization Algorithm for MLP considering the Advantage from Relationship

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-11页

作者： Luo, Yabo Waden, Yongo P. Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China

An improved ant colony optimization (ACO) is presented to solve the machine layout problem (MLP), and the concept is categorized as follows: firstly, an ideology on "advantage from quantity" and "advantage from relationship" is proposed and an example is demonstrated. In addition, the strategy of attached variables under local polar coordinate systems is employed to maintain search efficiency, that is, "advantage from relationship";thus, a mathematical model is formulated under a single rectangular coordinate systemin which the relative distance and azimuth between machines are taken as attached design variables. Further, the aforementioned strategies are adopted into the ant colony optimization (ACO) algorithm, thereby employing the inverse feedback mechanism for dissemination of pheromone and the positive feedback mechanism for pheromone concentration. Finally, the effectiveness of the proposed improved ACO is tested through comparative experiments, in which the results have shown both the reliability of convergence and the improvement in optimization degree of solutions.

关键词： MANUFACTURING processes ant algorithms POLAR coordinates (Mathematics) RELIABILITY (Engineering) MATHEMATICAL optimization

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