Performance Analysis of Chaotic Lévy Bat Algorithm and Chaotic Cuckoo Search Algorithm for Gray Level Image Enhancement

Local search-based dynamically adapted bat algorithm in image enhancement domain

“…Combination of entropy and edge information is considered as objective function which is given below (Dhal et al, 2015d):…”

“…In this study, image enhancement techniques (Gonzalez and Woods, 2002) have been formulated as optimisation problems and solved by nature inspired optimisation algorithms. Genetic algorithm (GA), particle swarm optimisation (PSO), differential evolution (DE), Cuckoo search (CS), artificial bee colony (ABC), firefly algorithm (FA) are some nature inspired metaheuristic algorithms which were effectively used in image enhancement field Ghosh, 2009, 2011;Munteanu and Rosa, 2001;Pal et al, 1994;Hashemi et al, 2010;Coelho et al, 2009;Dhal et al, 2015aDhal et al, , 2015bDhal et al, , 2015cDhal et al, , 2015dDhal et al, , 2017bDhal et al, , 2017aShanmugavadivu et al, 2014;Braik et al, 2007;Quraishi et al, 2012;Das, 2016, 2015;Quraishi et al, 2013). PSO gave better results than GA in the image enhancement domain by maximising employed entropy-based objective function.…”

“…CS and FA also outperformed PSO by considering the ability to maximise or minimise some benchmark mathematical functions (Yang, 2010c(Yang, , 2010b. The performance of these algorithms can be increased by some mechanism such as use of different random number generators to modify the solutions (Coelho et al, 2009;Dhal et al, 2015bDhal et al, , 2015cDhal et al, , 2015d, use of different inertia weight to get a better convergence rate (Dhal et al, 2015d;Bansal et al, 2011), use of communication between solutions (Caponetto et al, 2003) and so on. Mutation factor and crossover rate had been modified by chaotic sequence of traditional DE algorithm and experimental result showed that modified DE was far better than traditional DE in image enhancement field with faster convergence rate and also maintained a good diversity property (Coelho et al, 2009).…”

“…Lévy flight with chaotic step length had been used to generate new solutions and one population diversity measurement technique had been used as a safe guard from premature convergence in Dhal et al (2015bDhal et al ( , 2015c to modify FA and DE respectively, which outperformed the traditional FA and DE in the image enhancement domain by maximising the employed objective function with faster convergence rate. A newly proposed metaheuristic algorithm called bat algorithm (BA) which is developed based on the echolocation behaviour of bats, already outperformed GA and PSO (Yang, 2010a(Yang, , 2013, also gave better results than CS algorithm in image enhancement field (Dhal et al, 2015d). Some modifications were also reported on BA in literature to improve its performance (Fister et al, 2014(Fister et al, , 2013Yilmaz et al, 2014;Liu et al, 2012;Gandomi and Yang, 2014).…”

A Hybrid SCA Inspired BBO for Feature Selection Problems

“…Many metaheuristic search algorithms have been employed for FS to search for (near) optimal subset of features from these large volume datasets, as they prove their superiority in bringing out a better performance. Some of the most popular metaheuristic algorithms are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO), algorithms inspired by fish schools [3], Gravity search [4], different aspects of the behaviour of bees [5], Fireflies [6], Bats [7], Cuckoo birds [8], etc. Newly proposed modifications in search heuristics like chaotic maps [9], Sine Cosine Algorithm [10], Evolutionary methods [11], Local searches [12], and Biogeography Based Optimization [13] have also improved the performance of the search heuristics internally.…”