A multilevel image thresholding using the animal migration optimization algorithm

2018 ◽  
Vol 2 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Taymaz Rahkar Farshi
Keyword(s):  
Optimization Algorithm ◽  
Image Thresholding ◽  
Animal Migration Optimization ◽  
Animal Migration

scholarly journals Opposition-Based Animal Migration Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Cao ◽  
Xiangtao Li ◽  
Jianan Wang
Keyword(s):  
Convergence Rate ◽  
Optimization Algorithm ◽  
Experimental Verification ◽  
Search Space ◽  
Migration Behavior ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Animal Migration Optimization ◽  
Accelerate Convergence ◽  
Animal Migration

AMO is a simple and efficient optimization algorithm which is inspired by animal migration behavior. However, as most optimization algorithms, it suffers from premature convergence and often falls into local optima. This paper presents an opposition-based AMO algorithm. It employs opposition-based learning for population initialization and evolution to enlarge the search space, accelerate convergence rate, and improve search ability. A set of well-known benchmark functions is employed for experimental verification, and the results show clearly that opposition-based learning can improve the performance of AMO.

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