Paper Info
Title
Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning.
Abstract
Bat Algorithm BA is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning OBL is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.
Year
DOI
Venue
2016
10.1155/2016/8031560
Scientific Programming
Field
DocType
Volume
Convergence (routing),Mathematical optimization,Bat algorithm,Computer science,Opposition based learning,Local optimum,Random walk,Lévy flight,Swarm intelligence,Optimization problem
Journal
2016
ISSN
Citations 
PageRank 
1058-9244
1
0.35
References 
Authors
11
3
Name
Order
Citations
PageRank
Xian Shan110.35
Kang Liu210.35
Pei-Liang Sun310.35