An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

2021 ◽  
pp. 1-16
Author(s):  
Ghizlane Khababa ◽  
Fateh Seghir ◽  
Sadik Bessou
Keyword(s):  
Web Services ◽  
Local Search ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Evolutionary Strategy ◽  
Extended Version ◽  
The Public ◽  
Bee Colony ◽  
Skyline Operator ◽  
Experimental Comparisons

 In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

A Memetic Artificial Bee Colony Algorithm for High Dimensional Problems

2020 ◽  
Vol 19 (01) ◽  
pp. 2050008
Author(s):  
Dongli Jia ◽  
Teng Li ◽  
Yufei Zhang ◽  
Haijiang Wang
Keyword(s):  
Local Search ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Small Region ◽  
High Dimensional ◽  
Large Space ◽  
Bee Colony ◽  
Local Search Strategy

This work proposed a memetic version of Artificial Bee Colony algorithm, or called LSABC, which employed a “shrinking” local search strategy. By gradually shrinking the local search space along with the optimization process, the proposed LSABC algorithm randomly explores a large space in the early run time. This helps to avoid premature convergence. Then in the later evolution process, the LSABC finely exploits a small region around the current best solution to achieve a more accurate output value. The optimization behavior of the LSABC algorithm was studied and analyzed in the work. Compared with the classic ABC and several other state-of-the-art optimization algorithms, the LSABC shows a better performance in terms of convergence rate and quality of results for high-dimensional problems.

scholarly journals School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 73
Author(s):  
Kaixiang Zhu ◽  
Lily D. Li ◽  
Michael Li
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Proposed Model ◽  
School Timetabling ◽  
School Timetabling Problem ◽  
World School

Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

Sign in / Sign up

Export Citation Format

Share Document