An ant colony optimization algorithm for the bi-objective shortest path problem

Ghoseiri, Keivan; Nadjari, Behnam

doi:10.1016/j.asoc.2009.09.014

Cited by 84 publications

(23 citation statements)

References 20 publications

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“…For example, in Figure 4, there exist four paths between the start and the end points: 'start-A-D-end', 'start-A-C-end', 'start-A-C-E-end' and 'start-B-E-end' with their respective related objective functions of (5, 5), (4, 5), (6,6) and (6,4). In this graph, the objective functions (5,5) and (6,6) are dominated by (4,5), however between (4,5) and (6,4) the best path cannot be chosen because none is dominated by the other. As a result, the non-dominated paths are 'start-A-C-end' and 'start-B-E-end' with (4,5), (6,4) as objective functions.…”

Section: A Shortest Path Problemmentioning

confidence: 99%

Automated application offloading through ant-inspired decision-making

Golchay

Mouelt

Ponge

et al. 2016

2016 13th International Conference on New Technologies for Distributed Systems (NOTERE)

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Abstract-The explosive trend of smartphone usage as the most effective and convenient communication tools of human life in recent years make developers build ever more complex smartphone applications. Gaming, navigation, video editing, augmented reality, and speech recognition applications require considerable computational power and energy. Although smartphones have a wide range of capabilities -GPS, WiFi, cameras -their inherent limitations -frequent disconnections, mobilityand significant constraints -size, lower weights, longer battery life -make difficult to exploiting their full potential to run complex applications. Several research works have proposed solutions in application offloading domain, but few ones concerning the highly changing properties of the environment. To address these issues, we realize an automated application offloading middleware, ACOMMA, with dynamic and re-adaptable decision-making engine. The decision engine of ACOMMA is based on an antinspired algorithm. I. INTRODUCTIONThe explosive trend of smartphone usage as the most effective and convenient communication tools of human life in recent years -with the 50 percent growth rate in 2013 [1] -make developers to build ever more complex smartphone applications such as gaming, navigation, video editing, augmented reality, and speech recognition, which require considerable computational power and energy.However smartphones have a wide range of capabilities, typically including GPS, WiFi, cameras, gigabytes of storage, and gigahertz-speed processors, the importance and desirability of smaller sizes, lower weights and longer battery life as well as their inherent limitations such as resource scarcity, frequent disconnections and mobility, make them difficult to exploiting their full potential to run these complex applications and have the best performance.It seems to keep pace with increasing performance requirements, mobile users have to continually upgrade their hardware to augment the computational power as applications become more complex but still experience some limitations specially short battery lifetime. More feasible approach is empowering mobile devices using software solutions-application offloading, that improves the performance and the energy consumption of resource-poor mobile devices by using the power of one or more resource-rich stations. A key area of application offloading is to apply a remote execution of an application -totally or partially -to resource-intensive devices to improve performance and energy consumption. The surrogate can be a powerful stationary device or a set of processors. Drastic evolution of wireless technologies that

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Section: A Shortest Path Problemmentioning

confidence: 99%

Automated application offloading through ant-inspired decision-making

Golchay

Mouelt

Ponge

et al. 2016

2016 13th International Conference on New Technologies for Distributed Systems (NOTERE)

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“…This method is widely applied to the complex multi-objective optimization problem. Now some more mature evolutionary algorithms used in the multiobjective optimization problem include genetic algorithm [14] ant colony algorithm [15,16] particle swarm algorithm [17,18] etc. For example, Wu [19] et al proposes an improved constraint optimization particle swarm algorithm.…”

Section: Related Workmentioning

confidence: 99%

Tourism Service Composition Based on Multi-objective Optimization

Zheng

Peng

et al. 2015

2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems

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Tourism service composition combines various tourism elements for users, including transportations, catering, accommodation and other tourism service elements, in order to generate a one-stop tourism service that meets multi-objective needs of users. In doing this, it should simultaneously consider the historical travel data and the current preference of user to complete the composition. In addition, the composition process should be efficient enough to return results online for users in real time. In this paper, we establish a model of tourism service composition based on multi-objective optimization according to the parameters of tourism services. In particular, this model involves three optimization objectives: time, cost and quality of services. We then propose a single-objective-based optimization algorithm to effectively and efficiently solve the tourism service composition problem based on multi-objective optimization. Experimental results demonstrate that, in comparison with the particle swarm optimization-based multi-objective optimization algorithm, our approach can obtain more accurate results and achieve the significant improvement in optimization efficiency.

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“…Thus, the paths with high concentration pheromone can attract more ants to choose them. The positive feedback principle of the ant pheromone can guide the ants to find the optimal paths, so it has fast convergence and global optimization performances [8][9][10]. However, like any other swarm intelligent optimization algorithms, such as genetic algorithm, the artificial fish school algorithm, and so on, the ant colony algorithm has the prematurity problem [11][12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive ant colony optimization algorithm

Xiu

Cheng

et al. 2014

2014 International Conference on Mechatronics and Control (ICMC)

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An adaptive ant colony algorithm is proposed to overcome the premature convergence problem in the conventional ant colony algorithm. The adaptive ant colony is composed of three groups of ants: ordinary ants, abnormal ants and random ants. Each ordinary ant searches the path with the high concentration pheromone at the high probability, each abnormal ant searches the path with the high concentration pheromone at the low probability, and each random ant randomly searches the path regardless of the pheromone concentration. Three groups of ants provide a good initial state of pheromone trails together. As the optimization calculation goes on, the number of the abnormal ants and the random ants decreases gradually. In the late optimization stage, all of ants transform to the ordinary ants, which can rapidly concentrate to the optimal paths. Simulation results show that the algorithm has a good optimization performance, and can resolve traveling salesman problem effectively.

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An ant colony optimization algorithm for the bi-objective shortest path problem

Cited by 84 publications

References 20 publications

Automated application offloading through ant-inspired decision-making

Automated application offloading through ant-inspired decision-making

Tourism Service Composition Based on Multi-objective Optimization

Adaptive ant colony optimization algorithm

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