A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

Kamoona, Ammar Mansoor; Patra, J.C.

doi:10.1016/j.asoc.2019.105749

Cited by 55 publications

(15 citation statements)

References 32 publications

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“…Maurya et al [16] Cuckoo search (CS) algorithm CSA is used to balance the contrast and brightness Nickfarjam et al [17] Modified PSO algorithm Consists of the standard deviation and edge content Sathiyabhama, B et al [18] Gray wolf optimizer algorithm Improve with rough set theory Qin et al X [19] Modified PSO algorithm A modified inertia weight function used in the PSO Acharya et al [20] Modified genetic (GA) algorithm Adaptive histogram equalization technique used in the GA Muniyappan et al [21] Adaptive genetic algorithm Introduce adaptive crossover and mutation operations in GA Bhandari et al [22] CS algorithm Improve the contrast of low-contrast image using CSA Kamoona et al [23] Modified CS algorithm Image transform enhancement functions and objective function Prasath et al [24] Modified CS algorithm Distance-Oriented Cuckoo Search (DOCS) algorithm Sridevi et al [25] Modified genetic algorithm Fractional Genetic Algorithm Chen et al [26] Artificial bee colony algorithm A new fitness function and new image transformation function Banharnsakun et al [27] Artificial bee colony algorithm Image edge detection enhancement using ABC algorithm…”

Section: Authors Algorithms Strategymentioning

confidence: 99%

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Xiong

Zhang

et al. 2021

Mathematics

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At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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Section: Authors Algorithms Strategymentioning

confidence: 99%

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Xiong

Zhang

et al. 2021

Mathematics

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“…The optimizations among the 13 benchmark functions are implemented utilizing the standard CS, the WCSDE and other four CS variants (enhance Cuckoo Search (ECS) [32], modified Cuckoo Search (MCS) [33], a hybrid optimization algorithm based on Differential Evolution and Cuckoo Search (DECS) [25], and Snap-drift cuckoo search (SDCS) [34]. In the first experiment, 1000 iterations were set as the criterion, and each algorithm was run independently 30 times.…”

Section: Function Optimization Using the Wcsdementioning

confidence: 99%

“…This proposed WCSDE makes up for the shortcomings of information exchange between populations in the standard CS algorithm and enhances information utilization to obtain better convergence accuracy. In experimental part, 13 classic benchmark functions are selected to execute function optimization tasks using the standard CS [1], the WCSDE, and other four CS variants (ECS [32], MCS [33], DECS [25], and SDCS [34]) for verifying the optimization ability of the WCSDE algorithm. Experimental results show that the performance of the WCSDE algorithm is much better than other variants after numerical and statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

Zhang

Zhou²,

et al. 2021

IEEE Access

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This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities. Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE. On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird's nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability. On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy. In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects. The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness.

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“…With respect to applications, CS has been extensively applied to many domains, such as neural networks [87], image processing [88], nonlinear systems [89,90], network structural optimization [91], agriculture optimization [92], engineering optimization [93], and scheduling [94]. These applications indicate that CS algorithm is an effective and efficient optimizer for solving some real-world problems.…”

Section: Related Workmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

Cited by 55 publications

References 32 publications

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

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