Hybrid Ant Colony Optimization, Genetic Algorithm, and Simulated Annealing for image contrast enhancement

Abstract

In this paper, we propose a hybrid algorithm including Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Simulated Annealing (SA) metaheuristics for increasing the contrast of images. In this way, the contrast enhancement is obtained by globally transformation of the input intensities. ACO is used to generate the transfer functions which map the input intensities to the output intensities. SA as a local search method is utilized to modify the transfer functions generated by ACO. GA has the responsibility of evolutionary process of ants' characteristics. The results indicate that the new method performs better than the previously presented methods from the subjective and objective viewpoints.

An Intelligent Novel Tripartite - (PSO-GA-SA) Optimization Strategy

“…The deficiencies of this approach are the lack of diversity using only the initial ACO solutions and the implementation of only one level of fitness evaluation in the entire process. In all these, no work is currently available that explores the combined simultaneous advantages of using PSO, GA and SA for optimisation; this approach also accommodates the inadequacies highlighted in (Hoseini and Shayesteh, 2010). This research paper designs a novel tripartite PSO-GA-SA approach that delivers improved results by simultaneously exploring the individual advantages of each of the approaches for more diversity; this further prevents the approach from being stuck in a local minimum solution.…”

“…Since the numerous practical problems differ, this reality therefore leads to many approaches for solving many practical optimisation problems (Huesken, Jin and Sendhoff, 2002;Sbalzarini, Muller and Koumoutsakos, 2000). These approaches are either used individually or are combined with one another to provide better solutions to problems such as the work of (Hoseini and Shayesteh, 2010). However, the use of these approaches depends entirely on the specific problem application.…”

“…Just as in the case of (Hoseini and Shayesteh, 2010), ACO was used to generate an initial solutions for GA-SA and then the result was further utilised by the PSO algorithm. The deficiencies of this approach are the lack of diversity using only the initial ACO solutions and the implementation of only one level of fitness evaluation in the entire process.…”

“…Also, the work of Yang et al (2014) employed another strategy which involved the development of an improved version of a hybrid of PSO and SA by exploring the temperature cooling rate of SA in order to help in increasing the probability of obtaining a global optima solution. A hybrid version of the combinations of ACO, GA and SA (Hoseini and Shayesteh, 2010) was used for image contrast enhancement by the transformation of the input intensities.…”

Enriched Fuzzy and L*A*B based Mix Contrast Limited Adaptive Histogram Equilization

“…This technique is verified by comparing it with a manually derived gamma value, image quality, and derivation time. Pourya Hoseini et.al (2010) [8] proposed a cross algorithm including Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Simulated Annealing (SA) metaheuristics for increasing the contrast of images. This way, the contrast enhancement is obtained by globally transformation of the input intensities.…”