Multi-Objective QoS-based Reusable Service Selection enhanced with Artificial Bee Colony (MQRSABC)
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), V52(2),80-85 October 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
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.
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Keywords
Artificial Bee Colony, Particle Swarm
Optimization, Ant Colony Optimization, Multi-
Objective Constraints, Quality of Service, Reusable
Service Selection.