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

Wang, Limin; Shen, Jun; Luo, Junzhou

doi:10.1109/cbd.2014.15

Cited by 5 publications

(7 citation statements)

References 25 publications

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“…In multi-objective ant colony system (MOACS) problem, every solution by ant is measured according to more than one objective function, each of which must be minimized or maximized [28] [29]. In group formation problem using MOACS, the objective function is the objective of attribute that must be heterogeneous (maximized) or homogeneous (minimized).…”

Section: ) Initialization Phasementioning

confidence: 99%

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Fahmi¹,

Nurjanah²

2018

EECSI

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Collaborative learning is widely applied in education. One of the key aspects of collaborative learning is group formation. A challenge in group formation is to determine appropriate attributes and attribute types to gain good group results. This paper studies the use of an improved ant colony system (ACS), called Multi Objective Ant Colony System (MOACS), for group formation. Unlike ACS that transforms all attribute values into a single value, thus making any attributes are not optimally worth, MOACS tries to gain optimal values of all attributes simultaneously. MOACS is designed for various combinations of attributes and can be used for homogeneous, heterogeneous or mixed attributes. In this paper, sensing/intuitive learning styles (LSSI) and interests in subjects (I) are used in homogeneous group formation, while active/reflective learning style (LSAR) and previous knowledge (KL) are used for heterogeneous or mixed group formation. Experiments were conducted for measuring the average goodness of attributes (avgGA) and standard deviation of goodness of attributes (stdGA). The objectives of MOACS for homogeneous attributes were minimum avgGA and stdGA, while those for heterogeneous attributes were maximum avgGA and minimum stdGA. As a conclusion, MOACS was appropriate for group formation with homogeneous or mixed.

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Section: ) Initialization Phasementioning

confidence: 99%

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Fahmi¹,

Nurjanah²

2018

EECSI

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“…In the regions where the points of the two surfaces were found to differ from each other, there are three situations: 1) if the points of the median attainment surfaces of MOACS dominate those of MOGA, then the label MOACS is put near the points, 2) if the points of the median attainment surfaces of MOGA dominate those of MOACS, then the label MOGA is put near the points, 3) if the points of the median attainment surfaces of MOACS are not dominated by those of MOGA and the points of the median attainment surfaces of MOGA are not dominated by those of MOACS, then no label is put. The details of all the median summary attainment surface of MOACS and MOGA were given in [42] The lessons learned from the experimental results are that, when we have a large number of concrete services available for each abstract service, a multi-objective genetic algorithm can achieve better solutions. On the other hand, whenever the number of concrete services available is small, such as in some simple and repetitive scientific computation, a multi-objective ant colony system is to be preferred to a multi-objective genetic algorithm.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The first four metrics measure the convergence of the Pareto-optimal solutions, while the fifth metric measures the distribution of the Pareto-optimal set obtained by a multi-objective optimization algorithm. The details of these metrics were given in [42]. A comprehensive comparison of the two algorithms were provided in the following.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The details of the new rules for MOACS were described in [42]. The implementation of our proposed MOACS is given in Algorithm 1.…”

Section: B Multi-objective Ant Colony Optimization Algorithmsmentioning

confidence: 99%

“…while ant k is not at the end vertex do 9: construct a solution according to the state transition rule described in [42]; 10: record the solution to P ; 11: apply the local updating rule described in [42]; 12: end while 13: end for 14: when all ants arrive at the end vertex, find the non-dominated set from P ; 15: update the global non-dominated set GP ; 16: apply the global updating rule described in [42] to GP ; 17: if step > M axIt then 18: break; // M axIt is the maximum number of iterations 19: end if 20: end while 21: output all solutions in the global non-dominated set.…”

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confidence: 99%

See 2 more Smart Citations

Bio‐inspired cost‐aware optimization for data‐intensive service provision

Wang

Shen

Luo

2015

Concurrency and Computation

Self Cite

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The use of Big Data and the development of cloud computing have led to greater dependence on dataintensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss singleobjective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics. The use of Big Data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss single-objective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics. Disciplines Engineering | Science and Technology Studies

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Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

Navimipour

2018

Int J Communication

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Summary Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.

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

Cited by 5 publications

References 25 publications

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Bio‐inspired cost‐aware optimization for data‐intensive service provision

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

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