A modified ant colony system for finding the expected shortest path in networks with variable arc lengths and probabilistic nodes

The problem of finding the expected shortest path in stochastic networks, where the presence of each node is probabilistic and the arc lengths are random variables, have numerous applications, especially in communication networks. The problem being NP-hard we use an ant colony system (ACS) to propose a metaheuristic algorithm for finding the expected shortest path. A new local heuristic is formulated for the proposed algorithm to consider the probabilistic nodes. The arc lengths are randomly generated based on the arc length distribution functions. Examples are worked out to illustrate the applicability of the proposed approach.     

A novel multiple rule sets data classification algorithm based on ant colony algorithm

Ant colony optimization (ACO) algorithms have been successfully applied in data classification, which aim at discovering a list of classification rules. However, due to the essentially random search in ACO algorithms, the lists of classification rules constructed by ACO-based classification algorithms are not fixed and may be distinctly different even using the same training set. Those differences are generally ignored and some beneficial information cannot be dug from the different data sets, which may lower the predictive accuracy. To overcome this shortcoming, this paper proposes a novel classification rule discovery algorithm based on ACO, named AntMiner_mbc, in which a new model of multiple rule sets is presented to produce multiple lists of rules. Multiple base classifiers are built in AntMiner_mbc, and each base classifier is expected to remedy the weakness of other base classifiers, which can improve the predictive accuracy by exploiting the useful information from various base classifiers. A new heuristic function for ACO is also designed in our algorithm, which considers both of the correlation and coverage for the purpose to avoid deceptive high accuracy. The performance of our algorithm is studied experimentally on 19 publicly available data sets and further compared to several state-of-the-art classification approaches. The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.     

Ant Colony Optimization-based method for optic cup segmentation in retinal images

An accurate detection of the cup region in retinal images is necessary to obtain relevant measurements for glaucoma detection. In this work, we present an Ant Colony Optimization-based method for optic cup segmentation in retinal fundus images. The artificial agents will construct their solutions influenced by a heuristic that combines the intensity gradient of the optic disc area and the curvature of the vessels. On their own, the exploration capabilities of the agents are limited; however, by sharing the experience of the entire colony, they are capable of obtaining accurate cup segmentations, even in images with a weak or non-obvious pallor. This method has been tested with the RIM-ONE dataset, yielding an average overlapping error of 24.3% of the cup segmentation and an area under the curve (AUC) of 0.7957 using the cup to disc ratio for glaucoma assessment.     

Heuristic approach for solving employee bus routes in a large-scale industrial factory

This paper compares different methods for solving a location-routing problem (LRP), using real-world data from the bus transport service for employees of a large-scale industrial factory in Thailand. We tested four AI (artificial intelligence) techniques Maximin, K-means, Fuzzy C-means, and Competitive Learning and two hybrids of these four K-means with Competitive Learning and K-means with Maximin to allocate the bus stops. The efficiency of the algorithms was compared, in terms of the quality of the solutions. The K-means with Maximin provided the best solution, as it minimized number of bus stop locations and employees’ total traveling distance while satisfied employee at maximum radius 1.73 km, compared to K-means with Competitive Learning, as the same number of bus stop it provided higher total traveling distance and maximum radius. The other non-hybrid techniques provided higher number of bus stop locations.We then used ant colony optimization (ACO) to determine the optimal routing between the 300–700 bus stops as allocated by K-means with Maximin. The optimal bus routing to transport the factory’s 5000 plus employees required 134 buses (134 independent routes) covering 500 bus stops and traveling nearly 5000 km. While optimal, this routing was costly and created monitoring difficulties. To address these concerns, we constrained the number of bus routes; while this dramatically increased the total distance, it provided a more practical solution for the factory.     

Swarm intelligence based approach for sinkhole attack detection in wireless sensor networks

Swarm intelligence, a nature inspired computing applies an algorithm situated within the context of agent based models that mimics the behavior of ants to detect sinkhole attacks in wireless sensor networks. An Ant Colony Optimization Attack Detection (ACO-AD) algorithm is proposed to identify the sinkhole attacks based on the nodeids defined in the ruleset. The nodes generating an alert on identifying a sinkhole attack are grouped together. A voting method is proposed to identify the intruder. An Ant Colony Optimization Boolean Expression Evolver Sign Generation (ABXES) algorithm is proposed to distribute the keys to the alerted nodes in the group for signing the suspect list to agree on the intruder. It is shown that the proposed method identifies the anomalous connections without generating false positives and minimizes the storage in the sensor nodes in comparison to LIDeA architecture for sinkhole attack detection. Experimental results demonstrating the Ant Colony Optimization approach of detecting a sinkhole attack are presented.     

Framework for computationally efficient optimal crop and water allocation using ant colony optimization

A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application to optimal crop and water allocation. The framework achieves this goal by representing the problem in the form of a decision tree, including dynamic decision variable option (DDVO) adjustment during the optimization process and using ant colony optimization (ACO) as the optimization engine. A case study from literature is considered to evaluate the utility of the framework. The results indicate that the proposed ACO-DDVO approach is able to find better solutions than those previously identified using linear programming. Furthermore, ACO-DDVO consistently outperforms an ACO algorithm using static decision variable options and penalty functions in terms of solution quality and computational efficiency. The considerable reduction in computational effort achieved by ACO-DDVO should be a major advantage in the optimization of real-world problems using complex crop simulation models.     

A Physarum-inspired optimization algorithm for load-shedding problem

Load-shedding is an intentional reduction approach which can maintain the stability of a microgrid system effectively. Recent studies have shown that a load-shedding problem can be solved by formulating it as a 0/1 knapsack problem (KP). Although approximate solutions of 0/1 KP can be given by ant colony optimization (ACO) algorithms, adopting them requests a delicate consideration of the robustness, convergence rate and premature convergence. This paper proposes a new kind of Physarum-based hybrid optimization algorithm, denoted as PM-ACO, based on the critical paths reserved feature of Physarum-inspired mathematical (PM) model. Through adding additional pheromone to those important items selected by the PM model, PM-ACO improves the selection probability of important items and emerge a positive feedback process to generate optimal solutions. Comparing with other 0/1 KP solving algorithms, our experimental results demonstrate that PM-ACO algorithms have a stronger robustness and a higher convergence rate. Moreover, PM-ACO provides adaptable solutions for the load-shedding problem in a microgrid system.     

Global Village Media: Coming Together in the Early 1970s at Whiz Bang Quick City

Felicity D Scott , Associate Professor of Architecture at Columbia University Graduate School of Architecture, Planning and Preservation (GSAPP), describes here the instant city events of the early 1970s. These foreshadowed current global village media practices in what now seems a quaintly material manner: communication between sites involved the physical mailing of videotapes and ‘wire photos’ through the postal service. The sites themselves – in the US and elsewhere – were transformed into ‘instant cities’ through the erection of physical geodesic domes, teepees and inflatables in seas of mud. The true antecedents of today's instant networking, these pioneers embraced the newest technology available at the time and the most innovative forms of temporary architecture.     

智能优化算法求解TSP问题

TSP（旅行商）问题代表组合优化问题,具有很强的工程背景和实际应用价值,但至今尚未找到非常有效的求解方法．为此,讨论了最近研究比较热门的使用各种智能优化算法（蚁群算法、遗传算法、模拟退火算法、禁忌搜索算法、Hopfield神经网络、粒子群优化算法、免疫算法等）求解TSP问题的研究进展,指出了各种方法的优缺点和改进策略．最后总结并提出了智能优化算法求解TSP问题的未来研究方向和建议．     

基于蚁群聚类算法的平面运动链中机构的识别

机构识别是机构型综合中的重要问课题,提出了一种基于蚁群聚类算法的平面运动链中机构识别的新方法,通过对点杆矩阵和最短路径矩阵聚类来判别运动链中相同的机构。该方法适用于杆数任意的平面运动链的机构识别。仿真结果表明,该方法具有很好的效果。