Paper Info
Title
Bat-inspired algorithms with natural selection mechanisms for global optimization.
Abstract
In this paper, alternative selection mechanisms in the bat-inspired algorithm for global optimization problems are studied. The bat-inspired algorithm is a recent swarm-based intelligent system which mimics the echolocation system of micro-bats. In the bat-inspired algorithm, the bats randomly fly around the best bat locations found during the search so as to improve their hunting of prey. In practice, one bat location from a set of best bat locations is selected. Thereafter, that best bat location is used by local search with a random walk strategy to inform other bats about the prey location. This selection mechanism can be improved using other natural selection mechanisms adopted from other advanced algorithms like Genetic Algorithm. Therefore, six selection mechanisms are studied to choose the best bat location: global-best, tournament, proportional, linear rank, exponential rank, and random. Consequently, six versions of bat-inspired algorithm are proposed and studied which are global-best bat-inspired algorithm (GBA), tournament bat-inspired algorithm (TBA), proportional bat-inspired algorithm (PBA), linear rank bat-inspired algorithm (LBA), exponential rank bat-inspired algorithm (EBA), and random bat-inspired algorithm (RBA). Using two sets of global optimization functions, the bat-inspired versions are evaluated and the sensitivity analyses of each version to its parameters studied. Our results suggest that there are positive effects of the selection mechanisms on the performance of the classical bat-inspired algorithm which is GBA. For comparative evaluation, eighteen methods are selected using 25 IEEE-CEC2005 functions. The results show that the bat-inspired versions with various selection schemes observing the “survival-of-the-fittest” principle are largely competitive to established methods.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.07.039
Neurocomputing
Keywords
Field
DocType
Bat-inspired algorithm,Selection Scheme,Swarm intelligence,Global optimization
Bat algorithm,Swarm behaviour,Swarm intelligence,Artificial intelligence,Population-based incremental learning,Genetic algorithm,Mathematical optimization,Global optimization,Natural selection,Algorithm,Local search (optimization),Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
273
0925-2312
10
PageRank 
References 
Authors
0.46
38
6
Name
Order
Citations
PageRank
Mohammed Azmi Al-Betar162043.69
mohammed a awadallah227322.16
Hossam Faris376138.48
Xin-She Yang4342.59
Ahamad Tajudin Khader568340.71
Osama Ahmad Alomari6271.68