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Reliability Maximization of Power System Using Ant Colony Approach
A. Zeblah
International Journal of Electrical and Power Engineering , 2012,
Abstract: This study describes and uses an ant colony meta-heuristic optimization method to solve the reliability optimization problem. This problem is known as total reliability maximization of parallel-series system configuration. Redundant elements are included to achieve a high desired level of reliability. System reliability is represented by a multi-state reliability function. The systems elements are characterized by their performance (capacity), reliability and cost. These elements are chosen among a list of products available on the market. The proposed meta-heuristic seeks to the best maximal reliability system configuration with limited system investment. To estimate the parallel-series system reliability, a fast method based on Universal Moment Generating Function (UMGF) is suggested. The ant colony approach is used as an optimization technique.
Multi-Objective Incomplete Probability Information Optimization Reliability Design Based on Ant Colony Algorithm  [PDF]
Qiang ZHANG, Shouju LI, Ying TIAN
Journal of Software Engineering and Applications (JSEA) , 2009, DOI: 10.4236/jsea.2009.25046
Abstract: In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.
Models for Ordering Multiple Products Subject to Multiple Constraints, Quantity and Freight Discounts  [PDF]
John Moussourakis, Cengiz Haksever
American Journal of Operations Research (AJOR) , 2013, DOI: 10.4236/ajor.2013.36051

One of the most important responsibilities of a supply chain manager is to decide “how much” (or “many”) of inventory items to order and how to transport them. This paper presents four mixed-integer linear programming models to help supply chain managers make these decisions for multiple products subject to multiple constraints when suppliers offer quantity discounts and shippers offer freight discounts. Each model deals with one of the possible combinations of all-units, incremental quantity discounts, all-weight and incremental freight discounts. The models are based on a piecewise linear approximation of the number of orders function. They allow any number of linear constraints and determine if independent or common (fixed) cycle ordering has a lower total cost. Results of computational experiments on an example problem are also presented.

Ant Larval Demand Reduces Aphid Colony Growth Rates in an Ant-Aphid Interaction  [PDF]
Tom H. Oliver,Simon R. Leather,James M. Cook
Insects , 2012, DOI: 10.3390/insects3010120
Abstract: Ants often form mutualistic interactions with aphids, soliciting honeydew in return for protective services. Under certain circumstances, however, ants will prey upon aphids. In addition, in the presence of ants aphids may increase the quantity or quality of honeydew produced, which is costly. Through these mechanisms, ant attendance can reduce aphid colony growth rates. However, it is unknown whether demand from within the ant colony can affect the ant-aphid interaction. In a factorial experiment, we tested whether the presence of larvae in Lasius niger ant colonies affected the growth rate of Aphis fabae colonies. Other explanatory variables tested were the origin of ant colonies (two separate colonies were used) and previous diet (sugar only or sugar and protein). We found that the presence of larvae in the ant colony significantly reduced the growth rate of aphid colonies. Previous diet and colony origin did not affect aphid colony growth rates. Our results suggest that ant colonies balance the flow of two separate resources from aphid colonies- renewable sugars or a protein-rich meal, depending on demand from ant larvae within the nest. Aphid payoffs from the ant-aphid interaction may change on a seasonal basis, as the demand from larvae within the ant colony waxes and wanes.
A New Ant Colony Optimization Algorithm for Stochastic Loader Problem

ZHAO Pei-xin,MA Jian-hua,ZHAO Bing-xin,

系统工程理论与实践 , 2006,
Abstract: The stochastic loader problem and the procedure for solutions were proposed in this paper.On the basis of basic ant colony optimization algorithm,the new ant colony optimization algorithm with inner and outer mutation was designed to solve this problem.Two numerical examples were provided to illustrate the efficiency and reliability of this new algorithm.
Ant Colony Optimization Algorithm with Finite Grade Pheromone

KE Liang-Jun,FENG Zu-Ren,FENG Yuan-Jing,

自动化学报 , 2006,
Abstract: In the paper, a new class of ant colony optimization algorithm is proposed, in which pheromone is classified into finite grades, pheromone updating is realized by changing the grades, and the updated quantity of pheromone is independent of the objective function values. It is proved by means of finite Markov chains theory that the algorithm converges to the global optimal solutions linearly. Compared with MMAS, ACS and some other ant colony optimization algorithms for the Traveling Salesman Problem, the calculating results demonstrate that the proposed algorithm is effective and robust.
An Effective Clustering Algorithm With Ant Colony  [cached]
Xiao-yong Liu,Hui Fu
Journal of Computers , 2010, DOI: 10.4304/jcp.5.4.598-605
Abstract: This paper proposes a new clustering algorithm based on ant colony to solve the unsupervised clustering problem. Ant colony optimization (ACO) is a population-based meta-heuristic that can be used to find approximate solutions to difficult combinatorial optimization problems. Clustering Analysis, which is an important method in data mining, classifies a set of observations into two or more mutually exclusive unknown groups. This paper presents an effective clustering algorithm with ant colony which is based on stochastic best solution kept--ESacc. The algorithm is based on Sacc algorithm that was proposed by P.S.Shelokar. It’s mainly virtue that best values iteratively are kept stochastically. Moreover, the new algorithm using Jaccard index to identify the optimal cluster number. The results of several times experiments in three datasets show that the new algorithm-ESacc is less in running time, is better in clustering effect and more stable than Sacc. Experimental results validate the novel algorithm’s efficiency. In addition, Three indices of clustering validity analysis are selected and used to evaluate the clustering solutions of ESacc and Sacc.
Hiba Al-Zurba,Taha Landolsi,Mohamed Hassan,Fouad Abdelaziz
International Journal on Applications of Graph Theory in Wireless ad hoc Networks and Sensor Networks , 2011,
Abstract: This paper studies the suitability of using a meta-heuristic ant colony technique in routing multimediacontent over wireless sensor networks. The presented technique is both energy and QoS-aware. Ant colonyalgorithm is used to find the optimal routing path. Optimality is in the sense of minimizing energyconsumption and increasing link quality and reliability. The proposed approach results in minimizingenergy consumption and prolonging the lifetime of the network. Moreover, the optimal path has a high linkquality and reliability which enhances video frame quality and ensures high probability of successfuldelivery of video frames. The importance given to energy consumption, link quality, and link reliabilitymetrics can be varied depending on the multimedia application requirements.
The Routing Protocol Based on Improved Ant Colony Algorithm for Ad hoc Networks
基于改进蚁群算法的Ad hoc路由协议的研究

Feng Yong,Liao Rui-hua,Rao Ni-ni,Wang Wei-hua,

电子与信息学报 , 2008,
Abstract: Many of the existing proposed routing protocols could not give well stability and reliability and not fit in the needs for Ad hoc network.Because of the problems of great overhead and the lower stability in Ad hoc routing technology,an improved ant colony algorithm is proposed to study an ant-based Ad hoc routing protocol. Compared with the AODV(Ad hoc On-Demand Distance Vector)routing protocol which is a very mature strategy in Ad hoc study,simulation results show that by bringing the node colony function ...
Experiment Study of Entropy Convergence of Ant Colony Optimization  [PDF]
Chao-Yang Pang,Chong-Bao Wang,Ben-Qiong Hu
Computer Science , 2009,
Abstract: Ant colony optimization (ACO) has been applied to the field of combinatorial optimization widely. But the study of convergence theory of ACO is rare under general condition. In this paper, the authors try to find the evidence to prove that entropy is related to the convergence of ACO, especially to the estimation of the minimum iteration number of convergence. Entropy is a new view point possibly to studying the ACO convergence under general condition. Key Words: Ant Colony Optimization, Convergence of ACO, Entropy
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