Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

Abstract

Metaheuristic algorithm is a powerful optimization method, in which it can solve problems
by exploring the ordinarily large solution search space of these instances, that are believed to
be hard in general. However, the performances of these algorithms signicantly depend on
the setting of their parameter, while is not easy to set them accurately as well as completely
relying on the problem's characteristic. To ne-tune the parameters automatically, many
methods have been proposed to address this challenge, including fuzzy logic, chaos, random
adjustment and others. All of these methods for many years have been developed indepen-
dently for automatic setting of metaheuristic parameters, and integration of two or more of
these methods has not yet much conducted. Thus, a method that provides advantage from
combining chaos and random adjustment is proposed. Some popular metaheuristic algo-
rithms are used to test the performance of the proposed method, i.e. simulated annealing,
particle swarm optimization, dierential evolution, and harmony search. As a case study of
this research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. In
general, the simulation results show that the proposed methods are better than the original
metaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.