Multi-Objective QoS-based Reusable Service
Selection enhanced with Artificial Bee Colony
(MQRSABC)

A. Florence Deepa; J.G.R. Sathiaseelan

doi:https://doi.org/10.14445/22315381/IJETT-V52P213

Research Article | Open Access | Download PDF

Volume 52 | Number 1 | Year 2017 | Article Id. IJETT-V52P213 | DOI : https://doi.org/10.14445/22315381/IJETT-V52P213

Multi-Objective QoS-based Reusable Service Selection enhanced with Artificial Bee Colony (MQRSABC)

A. Florence Deepa, J.G.R. Sathiaseelan

Citation :

A. Florence Deepa, J.G.R. Sathiaseelan, "Multi-Objective QoS-based Reusable Service Selection enhanced with Artificial Bee Colony (MQRSABC)," International Journal of Engineering Trends and Technology (IJETT), vol. 52, no. 1, pp. 80-85, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V52P213

Abstract

Multi-objective optimization has been a difficult problem and a research focus in the field of science and engineering. An enhanced Artificial Bee Colony (ABC) optimization algorithm, called the QoS-Based Reusable Service Selection with Multi-Objective Constraints using Artificial Bee Colony (MQRSABC) Algorithm, is proposed to measure the quality of the search time and execution time better. In this proposal, the fast non-dictated population selection strategy are applied to measure the quality of the solution and select the better ones. An innovative solution generation strategy is designed to exploit the neighbourhood of the existing solutions. Likewise, a novel fitness calculation method is presented to calculate the selecting probability for onlookers. The proposed algorithm is validated on benchmark functions in terms of search time, execution time and reusability constraint with the existing approaches PSO and ACO. The experimental results show that the proposed approach can find solutions with competitive convergence and diversity within a shorter period of time, when compared with the traditional multiobjective algorithms. Subsequently, it can be considered as a feasible alternative to solve the multi-objective optimization problems.

Keywords

Artificial Bee Colony, Particle Swarm Optimization, Ant Colony Optimization, Multi- Objective Constraints, Quality of Service, Reusable Service Selection.

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