Ant Colony Optimization

Dorigo, Marco; Birattari, Mauro; Stützle, Thomas

doi:10.1109/ci-m.2006.248054

Cited by 1,178 publications

(348 citation statements)

References 86 publications

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“…Objects understanding and detection in dynamic environment changes are usually based on the adaptive background subtraction and other objects recognition methods [17,21,35,[65][66][67][68]. A preliminary scheme for the practical integration of proposed models with these algorithms is presented in Figure 7, where smog as a global environmental change has significant implications to video behaviors recognition and loitering detection within a hovering period of two persons; only half of hovering behaviors is detected; only one person is red-highlighted and the other person is always in a green rectangle, indicating the degradation of video surveillance efficiency within the considered periods under any real challenging scenarios.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to +1 , = 1, 2, . . .) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

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Section: Simulation and Discussionmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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“…We can selectively transmit interest packets by ant colony optimization (ACO). (11)(12)(13) ACO is a metaheuristic technique for solving combinatorial optimization problems that can be reduced to finding good paths through graphs. An ACO algorithm chooses a route by using pheromone traces of previous ants among multiple paths.…”

Section: Application Of Ant Colony Optimizationmentioning

confidence: 99%

Reliable Node Management Architecture for Disaster Surveillance and Response Systems

Yang¹,

Byun²

2018

Sensors and Materials

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Keywords: disaster surveillance, multisensor network, reliable node management, energy saving, disaster response In this study, a system supporting disaster surveillance and response decisions is investigated. In a multisensor surveillance system, distributed sensor nodes such as cameras can sense, monitor, and collect data continuously. To recognize an urgent situation and make a decision in response, distributed reasoning agents should be able to analyze and process various surveillance data in real time. However, the loss of resources such as sensor nodes and communication paths may occur during the overall process. Therefore, a safer data delivery system with shorter delays is needed. In this paper, a more reliable node management architecture based on energy-efficient algorithms is proposed to fulfill this need. This architecture determines the path of a packet from either the events generating the packet, the local environmental conditions, or the states of the adjacent nodes to improve the stability, energy efficiency, and delay guarantee of the system. To find the path for the required data, an ant colony optimization algorithm is adopted. This algorithm reduces the decision time and cache processing time resulting in low energy consumption. The performance analysis of the proposed method shows that the scheme improves the system reliability by controlling the parameters of each node.

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“…The virtual machine can be placed on the physical machine on condition that the Constraint (10) is satisfied. For each virtual machine, a corresponding set of candidate physical machines is established, and each physical machine in the set satisfies the constraint condition (10). Once a physical machine is selected, the available resource is updated until all virtual machines are placed.…”

Section: Goalsmentioning

confidence: 99%

“…Inspired by the collective behavior of real ant colony, Dorigo proposed ant colony optimization algorithm [10] systematically. The mechanism of ant colony optimization algorithm consists of two basic stages: adaptation phase and cooperation stage.…”

Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

“…Beloglazov et al [8] proposed Modified Best Fit Decreasing (MBFD) algorithm to solve the virtual machine placement problem based on CPU utilization. Virtual machine placement belongs to combinatorial optimization problem, so the improved algorithm can combine with genetic algorithm [9], ant colony algorithm [10] or particle swarm optimization algorithm [11] which is effective to the problem. In [12], a two-level control system is proposed to manage the mappings of workloads to virtual machines and virtual machines to physical resources.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective Virtual Machine Placement for Load Balancing

Feng

2017

ITM Web Conf.

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Abstract. The virtual machine placement is closely related to the efficient and balanced utilization of physical resources. In this paper, the influence of two scenarios about resource utilization on load balancing is analyzed. A multi-objective ant colony optimization algorithm is proposed to solve the virtual machine placement problem, which balances the load among physical machines and the internal load of physical machine simultaneously. The proposed algorithm is compared with two single objective ant colony optimization algorithms, first fit algorithm and greedy algorithm under some instances. The results show that the proposed algorithm can search and find solutions that exhibit good balance among objectives while others cannot. This demonstrates the proposed algorithm can balance the load in the process of mapping virtual machines to physical machines.

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Ant Colony Optimization

Cited by 1,178 publications

References 86 publications

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Reliable Node Management Architecture for Disaster Surveillance and Response Systems

Multi-objective Virtual Machine Placement for Load Balancing

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