On the Dynamic Ant Colony Algorithm Optimization Based on Multi-pheromones

Xia, Yamei; Chen, Junliang; Meng, Xiangwu

doi:10.1109/icis.2008.112

Cited by 23 publications

(18 citation statements)

References 13 publications

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“…In addition, the studies [29], [53], [56] did not provide details about whether the four QoS attributes were considered or not. [45] 2008…”

Section: Specification For Literature Review Strategymentioning

confidence: 99%

“…Besides the listed items, the references in Table 2 differ from each other with respect to the single objective ACO algorithm which they were based on, such as ant colony system adopted by the authors of [44], ant system adopted by the authors of [21], [45], [46], and max-min ant system adopted by the authors of [34].…”

Section: Ant Colony Optimization Algorithmsmentioning

confidence: 99%

“…In [45], different pheromones were used to denote different QoS attributes. The simulation used eight abstract services, and the number of concrete services corresponding to each abstract service was randomly selected from 0 to 25.…”

Section: Ant Colony Optimization Algorithmsmentioning

confidence: 99%

“…Further investigation is required to deal with all situations of clone ants. [45] C, T, Av, Re the most satisfying solution…”

Section: Ant Colony Optimization Algorithmsmentioning

confidence: 99%

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A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bio-inspired algorithms are sub-optimal methods. This paper will firstly present a hierarchical taxonomy of Web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the data-intensive service concretization problems. Abstract-In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bioinspired algorithms are sub-optimal methods. This paper will first present a hierarchical taxonomy of web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the dataintensive service concretization problems.

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“…In addition, the studies [29], [53], [56] did not provide details about whether the four QoS attributes were considered or not. [45] 2008…”

Section: Specification For Literature Review Strategymentioning

confidence: 99%

Section: Ant Colony Optimization Algorithmsmentioning

confidence: 99%

Section: Ant Colony Optimization Algorithmsmentioning

confidence: 99%

“…Further investigation is required to deal with all situations of clone ants. [45] C, T, Av, Re the most satisfying solution…”

Section: Ant Colony Optimization Algorithmsmentioning

confidence: 99%

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A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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“…Some studies only considered sequential composition structure [25]- [28], and some studies did not consider the global quality of service (QoS) constraints [25], [27]- [30]. Only single solutions were given by other studies [29], [31], [32]. In [28], the ant system was applied to solve the multi-objective optimization problem, but we applied the ant colony system.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Ant Colony System for Data-Intensive Service Provision

Wang¹,

Shen²,

Luo³

2014

2014 Second International Conference on Advanced Cloud and Big Data

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Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective dataintensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multiobjective genetic algorithm with respect to five performance metrics Abstract-Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective data-intensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multi-objective genetic algorithm with respect to five performance metrics.

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People as Ants: A Multi Pheromone Ubiquitous Approach for Intelligent Transport Systems

Sousa

Bento

Advances in Soft Computing

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On the Dynamic Ant Colony Algorithm Optimization Based on Multi-pheromones

Cited by 23 publications

References 13 publications

A Systematic Review of Bio-Inspired Service Concretization

A Systematic Review of Bio-Inspired Service Concretization

Multi-objective Ant Colony System for Data-Intensive Service Provision

People as Ants: A Multi Pheromone Ubiquitous Approach for Intelligent Transport Systems

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