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Division of Labor in a Group of Robots Inspired by ants' Foraging Behavior

ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS 2006年第1期1卷 4-25页

作者： Labella, Thomas H. Dorigo, Marco Deneubourg, Jean-Louis Univ Libre Bruxelles IRIDIA B-1050 Brussels Belgium Univ Libre Bruxelles CENOLI B-1050 Brussels Belgium

In this article, we analyze the behavior of a group of robots involved in an object retrieval task. The robots' control system is inspired by a model of ants' foraging. This model emphasizes the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group and that it brings forth division of labor between the members of the group. Moreover, robots that are best at retrieving have a higher probability of becoming active retrievers. This selection of the best members does not use any explicit representation of individual capabilities. We analyze this system and point out its strengths and its weaknesses.

关键词： algorithms Performance Adaptive systems ant algorithms bio-inspired systems adaptation

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ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2013年第5期9卷 1-13页

作者： Xu, Shaolun Feng, Donghan Yan, Zheng Zhang, Liang Li, Naihu Jing, Lei Wang, Jianhui Minist Educ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China Alstom Grid Technol Ctr Co Ltd Shanghai 200240 Peoples R China Argonne Natl Lab Decis & Informat Sci Div Argonne IL 60439 USA

Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm(ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method.

关键词： ant algorithms ELECTRIC vehicles MONTE Carlo method SWARM intelligence MATHEMATICAL optimization

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Enhanced ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018年第1期2018卷 9485478-9485478页

作者： Chen, Chi-Chung Liu, Yi-Ting Natl Chiayi Univ Dept Elect Engn 300 Syuefu Rd Chiayi 60004 Taiwan

This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACO(R)). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACO(R) and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.

关键词： MUTATION (Biology) ant algorithms FUZZY systems AD hoc networks (Computer networks) COMPUTER simulation

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Biased and greedy random walks on two-dimensional lattices with quenched randomness: The greedy ant within a disordered environment

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Physical Review E 2013年第6期88卷 062101-062101页

作者： T. L. Mitran O. Melchert A. K. Hartmann Institut für Physik Universität Oldenburg Carl-von-Ossietzky Strasse 26111 Oldenburg Germany Faculty of Physics University of Bucharest 077125 Magurele-Ilfov P. O. Box MG-11 Romania

The main characteristics of biased greedy random walks (BGRWs) on two-dimensional lattices with real-valued quenched disorder on the lattice edges are studied. Here the disorder allows for negative edge weights. In previous studies, considering the negative-weight percolation (NWP) problem, this was shown to change the universality class of the existing, static percolation transition. In the presented study, four different types of BGRWs and an algorithm based on the ant colony optimization heuristic were considered. Regarding the BGRWs, the precise configurations of the lattice walks constructed during the numerical simulations were influenced by two parameters: a disorder parameter ρ that controls the amount of negative edge weights on the lattice and a bias strength B that governs the drift of the walkers along a certain lattice direction. The random walks are “greedy” in the sense that the local optimal choice of the walker is to preferentially traverse edges with a negative weight (associated with a net gain of “energy” for the walker). Here, the pivotal observable is the probability that, after termination, a lattice walk exhibits a total negative weight, which is here considered as percolating. The behavior of this observable as function of ρ for different bias strengths B is put under scrutiny. Upon tuning ρ, the probability to find such a feasible lattice walk increases from zero to 1. This is the key feature of the percolation transition in the NWP model. Here, we address the question how well the transition point ρc, resulting from numerically exact and “static” simulations in terms of the NWP model, can be resolved using simple dynamic algorithms that have only local information available, one of the basic questions in the physics of glassy systems.

关键词： RANDOM walks (Mathematics) LATTICE theory PERCOLATION (Statistical physics) ant algorithms PHASE transformations (Physics) PROBABILITY theory

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A Multiple-Dimensional Tree Routing Protocol for Multisink Wireless Sensor Networks Based on ant Colony Optimization

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2012年第unknown期2012卷 1-10页

作者： Zhou, Hui Qing, Dongliang Zhang, Xiaomei Yuan, Honglin Xu, Chen Nantong Univ Sch Elect & Informat Nantong 226019 Jiangsu Peoples R China

Routing protocol is an important topic in the wireless sensor networks. For MultiSink wireless sensor networks, the routing protocol designs and implementations are more difficult due to the structure complexity. The paper deals with the problem of a multiple-dimensional tree routing protocol for multisink wireless sensor networks based on ant colony optimization. The proposed protocol is as follows: (1) listening mechanism is used to establish and maintain multidimensional tree routing topology;(2) taking into consideration hops, packet losses, retransmission, and delay account, a distributed ant colony algorithm is proposed. When nodes select routes in the data transmission, the algorithm is utilized to realize the real-time optimization by coordination between nodes. The simulation results show that the proposed protocol can realize the QoS optimization for multisink wireless sensor networks, and its performance is better than the routing protocol of minimum hop numbers.

关键词： ROUTING protocols (Computer network protocols) WIRELESS sensor networks ant algorithms ELECTRIC network topology NETWORK time delays DATA transmission systems NETWORK performance (Telecommunication)

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Energy Efficient Source Location Privacy Protecting Scheme in Wireless Sensor Networks Using ant Colony Optimization

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2014年第unknown期2014卷 1-14页

作者： Zhou, Liming Wen, Qiaoyan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Wireless sensor networks consist of various sensors with limited power, which collect different useful and privately relevant information. We pay attention to some issues related to sensor's location privacy. ant colony optimization provides a natural and intrinsic way of exploration of search space for preserving sensor's location privacy. In this paper, we focus on protecting the sensor's location by introducing suitable modifications to sensor routing to make it difficult for an eavesdropper to find the original location. And we propose an energy efficient preserving sensor's location privacy based on ant colony optimization scheme, which is a flexible routing strategy to protect the sensor's location. Simulation results show that our strategy can efficiently reduce the chance of packets being detected and prolong the network lifetime. And the adversary can find it difficult to find the exact location of the source node.

关键词： ENERGY consumption COMPUTERS & privacy ant algorithms INFORMATION theory MATHEMATICAL optimization

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ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

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COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014年第1期2014卷 572494-572494页

作者： Gopalakrishnan, Ravichandran C. Kuppusamy, Veerakumar SCAD Inst Technol Coimbatore 641664 Tamil Nadu India RVS Coll Engn & Technol Dept ECE Dindigul 624005 India

Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

关键词： ant algorithms MATHEMATICAL optimization LUNG diseases -- Diagnosis COMPUTED tomography DIAGNOSTIC imaging

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A Novel Method of Failure Sample Selection for Electrical Systems Using ant Colony Optimization

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016年第1期2016卷 1-7页

作者： Xiong, Jian Tian, Shulin Yang, Chenglin Liu, Cheng Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Chengdu Technol Univ Dept Commun Engn Chengdu 611731 Peoples R China

The influence of failure propagation is ignored in failure sample selection based on traditional testability demonstration experiment method. Traditional failure sample selection generally causes the omission of some failures during the selection and this phenomenon could lead to some fearful risks of usage because these failures will lead to serious propagation failures. This paper proposes a new failure sample selection method to solve the problem. First, the method uses a directed graph and ant colony optimization (ACO) to obtain a subsequent failure propagation set (SFPS) based on failure propagation model and then we propose a new failure sample selection method on the basis of the number of SFPS. Compared with traditional sampling plan, this method is able to improve the coverage of testing failure samples, increase the capacity of diagnosis, and decrease the risk of using.

关键词： FAILURE analysis (Engineering) ant algorithms COMPUTATIONAL intelligence NEUROSCIENCES ELASTIC wave propagation

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Intercluster ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2014年第4期10卷 1-10页

作者： Kim, Jung-Yoon Sharma, Tripti Kumar, Brijesh Tomar, G. S. Berry, Karan Lee, Won-Hyung Chung Ang Univ ChungAng Cultural Arts Ctr Dept Image Engn Off 503 Seoul 156756 South Korea Maharaja Surajmal Inst Technol Dept Informat Technol New Delhi 110058 India Lingayas Univ Dept Informat Technol Faridabad 121002 Haryana India Machine Intelligence Res Labs Gwalior 474011 India Univ W Indies Dept Elect & Comp Engg St Augustine 1000 Trinidad Tobago

Wireless sensor networks have grown rapidly with the innovation in Information Technology. Sensor nodes are distributed and deployed over the area for gathering requisite information. Sensor nodes possess a negative characteristic of limited energy which pulls back the network from exploiting its peak capabilities. Hence, it is necessary to gather and transfer the information in an optimized way which reduces the energy dissipation. ant Colony Optimization (ACO) is being widely used in optimizing the network routing protocols. ant Based Routing can play a significant role in the enhancement of network life time. In this paper, Intercluster ant Colony Optimization algorithm (IC-ACO) has been proposed that relies upon ACO algorithm for routing of data packets in the network and an attempt has been made to minimize the efforts wasted in transferring the redundant data sent by the sensors which lie in the close proximity of each other in a densely deployed network. The IC-ACO algorithm was studied by simulation for various network scenarios. The results depict the lead of IC-ACO as compared to LEACH protocol by indicating higher energy efficiency, prolonged network lifetime, enhanced stability period, and the elevated amount of data packets in a densely deployed wireless sensor network.

关键词： ant algorithms WIRELESS sensor networks COMPUTER algorithms INFORMATION technology WIRELESS sensor nodes ROUTING (Computer network management) DATA packets & packeting

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Energy Efficient Routing in Wireless Sensor Networks Based on Fuzzy ant Colony Optimization

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2014年第7期10卷 1-17页

作者： Amiri, Ehsan Keshavarz, Hassan Alizadeh, Mojtaba Zamani, Mazdak Khodadadi, Touraj Islamic Azad Univ Nourabad Mamasani Branch Dept Comp Engn Nourabad Mamasani Iran Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Adv Informat Sch Kuala Lumpur 54100 Malaysia

A wireless sensor network (WSN) is a collection of sensor nodes that dynamically self-organize themselves into a wireless network without the utilization of any preexisting infrastructure. One of the major problems in WSNs is the energy consumption, whereby the network lifetime is dependent on this factor. In this paper, we propose an optimal routing protocol for WSN inspired by the foraging behavior of ants. The ants try to find existing paths between the source and base station. Furthermore, we have combined this behavior of ants with fuzzy logic in order for the ants to make the best decision. In other words, the fuzzy logic is applied to make the use of these paths optimal. Our algorithm uses the principles of the fuzzy ant colony optimization routing (FACOR) to develop a suitable problem solution. The performance of our routing algorithm is evaluated by Network Simulator 2 (NS2). The simulation results show that our algorithm optimizes the energy consumption amount, decreases the number of routing request packets, and increases the network lifetime in comparison with the original AODV.

关键词： WIRELESS sensor networks ENERGY consumption ant algorithms SELF-organizing systems ROUTING protocols (Computer network protocols)

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