scholarly journals Contrast Enhancement of Images Using Meta-Heuristic Algorithm

2021 ◽  
Vol 38 (5) ◽  
pp. 1345-1351
Author(s):  
Sunkavalli Jaya Prakash ◽  
Manna Sheela Rani Chetty ◽  
Jayalakshmi A
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Search Space ◽  
Primary Objective ◽  
Suggested Approach ◽  
Visual Contrast ◽  
Krill Herd

One of the most important processes in image processing is image enhancement, which aims to enhance image contrast and quality of information. Due to the lack of adequate conventional image enhancement and the challenge of mean shift, intelligence-based image enhancement systems are becoming an essential requirement in image processing. This paper proposes a new approach for enhancing low contrast images utilizing a modified measure and integrating a new Chaotic Crow Search (CCS) and Krill Herd (KH) Optimization-based metaheuristic algorithm. Crow Search Algorithm is a cutting-edge meta-heuristic optimization technique. Chaotic maps are incorporated into the Crow Search Method in this work to improve its global optimization. The new Chaotic Crow Search Algorithm depends on chaotic sequences to replace a random location in the search space and the crow's recognition factor. Based on a new fitness function, Krill Herd optimization is utilized to optimize the tunable parameter. The fitness function requires different primary objective functions that use the image's edge, entropy, grey level co-occurrence matrix (GLCM) contrast, and GLCM energy for increased visual, contrast, and other descriptive information. The results proved that the suggested approach outperforms all-new methods in terms of contrast, edge details, and structural similarity, both subjectively and statistically.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Image Enhancement with Improved Global and Local Visual Effects

2021 ◽  
Vol 01 ◽  
Author(s):  
Muhammad Adeel ◽  
Yinglei Song
Keyword(s):  
Objective Function ◽  
Image Enhancement ◽  
Optimization Technique ◽  
Optimization Method ◽  
Original Image ◽  
Visual Effects ◽  
Swarm Optimization ◽  
New Approach ◽  
Visual Contrast ◽  
Global And Local

Background: In many applications of image processing, the enhancement of images is often a step necessary for their preprocessing. In general, for an enhanced image, the visual contrast as a whole and its refined local details are both crucial for achieving accurate results for subsequent classification or analysis. Objective: This paper proposes a new approach for image enhancement such that the global and local visual effects of an enhanced image can both be significantly improved. Methods: The approach utilizes the normalized incomplete Beta transform to map pixel intensities from an original image to its enhanced one. An objective function that consists of two parts is optimized to determine the parameters in the transform. One part of the objective function reflects the global visual effects in the enhanced image and the other one evaluates the enhanced visual effects on the most important local details in the original image. The optimization of the objective function is performed with an optimization technique based on the particle swarm optimization method. Results: Experimental results show that the approach is suitable for the automatic enhancement of images. Conclusion: The proposed approach can significantly improve both the global and visual contrasts of the image.

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

scholarly journals Genetic Algorithm – A Sensible Evolutionary Optimization Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 2805-2809
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Effective Population ◽  
Natural Systems ◽  
Heuristic Search Algorithm ◽  
Feasible Solutions

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

scholarly journals Projectile-target search algorithm: a stochastic metaheuristic optimization technique

2019 ◽  
Vol 9 (5) ◽  
pp. 3772
Author(s):  
Ayong Hiendro
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Search Space ◽  
Global Optimum ◽  
Control Parameters ◽  
Target Search ◽  
Projectile Motion ◽  
Metaheuristic Optimization ◽  
Convergence Process ◽  
Accelerate Convergence

This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithms

scholarly journals Breaking Data Encryption Standard with a Reduced Number of Rounds Using Metaheuristics Differential Cryptanalysis

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1697
Author(s):  
Kamil Dworak ◽  
Urszula Boryczka
Keyword(s):  
Memetic Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Memetic Algorithms ◽  
Data Encryption ◽  
Differential Cryptanalysis ◽  
Search Process ◽  
Suggested Approach ◽  
Data Encryption Standard ◽  
Metaheuristic Techniques

This article presents the author’s own metaheuristic cryptanalytic attack based on the use of differential cryptanalysis (DC) methods and memetic algorithms (MA) that improve the local search process through simulated annealing (SA). The suggested attack will be verified on a set of ciphertexts generated with the well-known DES (data encryption standard) reduced to six rounds. The aim of the attack is to guess the last encryption subkey, for each of the two characteristics Ω. Knowing the last subkey, it is possible to recreate the complete encryption key and thus decrypt the cryptogram. The suggested approach makes it possible to automatically reject solutions (keys) that represent the worst fitness function, owing to which we are able to significantly reduce the attack search space. The memetic algorithm (MASA) created in such a way will be compared with other metaheuristic techniques suggested in literature, in particular, with the genetic algorithm (NGA) and the classical differential cryptanalysis attack, in terms of consumption of memory and time needed to guess the key. The article also investigated the entropy of MASA and NGA attacks.

scholarly journals Diagnosis of Faulty Elements in Array Antenna using Nature Inspired Cuckoo Search Algorithm

2018 ◽  
Vol 8 (3) ◽  
pp. 1870 ◽  
Author(s):  
Shafqat Ullah Khan ◽  
M. K. A. Rahim ◽  
Murtala Aminu-Baba ◽  
Atif Ellahi Khan Khalil ◽  
Sardar Ali
Keyword(s):  
Mobile Communications ◽  
Linear Array ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Correction Method ◽  
Cuckoo Search Algorithm ◽  
Array Antenna ◽  
Single Element

Detection and correction of faulty elements in a linear array have great importance in radar, sonar, mobile communications and satellite. Due to single element failure, the whole radiation pattern damage in terms of side lobes level and nulls. Once we have detect the position of defective element, then correction method is applied to achieve the desired pattern. In this work, we introduce a nature inspired meta-heuristic cuckoo search algorithm to diagnose the position of defective elements in a linear array. The nature inspired cuckoo search algorithm is new to the optimization family and is used first time for fault detection in an array antenna. Cuckoo search algorithm is a global search optimization technique. The cost function is used as a fitness function which defines an error between the degraded far field power pattern and the estimated one. The proposed technique is used effectively for the diagnosis of complete, as well as, for partial faulty elements position. Different simulation results are evaluated for 40 elements Taylor pattern to validate and check the performance of the proposed technique.

scholarly journals Legendre Cooperative PSO Strategies for Trajectory Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Liu ◽  
Yongji Wang ◽  
Fuqiang Xie ◽  
Jiashi Gao
Keyword(s):  
Trajectory Optimization ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Control Variable ◽  
Search Space ◽  
Linear Interpolation ◽  
Multiple Control ◽  
Final Time ◽  
Control Variables ◽  
Additional Control

Particle swarm optimization (PSO) is a population-based stochastic optimization technique in a smooth search space. However, in a category of trajectory optimization problem with arbitrary final time and multiple control variables, the smoothness of variables cannot be satisfied since the linear interpolation is widely used. In the paper, a novel Legendre cooperative PSO (LCPSO) is proposed by introducing Legendre orthogonal polynomials instead of the linear interpolation. An additional control variable is introduced to transcribe the original optimal problem with arbitrary final time to the fixed one. Then, a practical fast one-dimensional interval search algorithm is designed to optimize the additional control variable. Furthermore, to improve the convergence and prevent explosion of the LCPSO, a theorem on how to determine the boundaries of the coefficient of polynomials is given and proven. Finally, in the numeral simulations, compared with the ordinary PSO and other typical intelligent optimization algorithms GA and DE, the proposed LCPSO has traits of lower dimension, faster speed of convergence, and higher accuracy, while providing smoother control variables.

Energy Aware Enhanced Krill Herd Algorithm Enabled Clustering for Unmanned Aerial Vehicles

2021 ◽  
pp. 17-25
Author(s):  
Mohamed Elhoseny ◽  
◽  
X. Yuan ◽  
Mohamed Abdel-basset ◽  
◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
High Mobility ◽  
Search Space ◽  
Energy Utilization ◽  
Energy Aware ◽  
Krill Herd Algorithm ◽  
Aerial Vehicles ◽  
Krill Herd ◽  
Energy Efficient Clustering

Recently, unmanned aerial vehicles (UAV) have gained maximum interest in diverse applications ranging from military to civilian areas. The presence of numerous energy-constrained UAVs in an adhoc manner poses several design issues. At the same time, the limited battery, high mobility, and adaptive characteristics of the UAVs need effective design of clustering techniques for UAVs. In this manner, this paper presents a levy flight with a krill herd optimization algorithm (LF-KHOA) for energy-efficient clustering in UAVs. The proposed LF-KHOA technique integrates the concepts of LF to the KHOA to enhance efficiency and search space exploration. In addition, the LF-KHOA technique derives a fitness function involving three input parameters to elect cluster heads (CHs) and organize clusters. The energy consumed by the UAVs depends on the distance from UAVs to nearby nodes. Therefore, the fitness function aims to decrease communication distance, which mitigates energy utilization when transmitting the information. To ensure the better performance of the LF-KHOA technique, an extensive set of simulations takes place, and the results are inspected in terms of different measures. The experimental results highlighted the betterment of the LF-KHOA technique over the current state of art techniques.

scholarly journals Improving Search-Based Schematic Layout by Parameter Manipulation

2015 ◽  
Vol 25 (06) ◽  
pp. 961-991 ◽  
Author(s):  
Daniel Chivers ◽  
Peter Rodgers
Keyword(s):  
Iterative Process ◽  
Ad Hoc ◽  
Search Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Hill Climbing ◽  
Grid Spacing ◽  
Schematic Layout ◽  
Number Of Iterations ◽  
Better Than

This paper reports on a method to improve the automated layout of schematic diagrams by widening the search space examined by the system. In search-based layout methods there are typically a number of parameters that control the search algorithm which do not affect the fitness function, but nevertheless have an impact on the final layout. We explore how varying three parameters (grid spacing, the starting distance of allowed node movement and the number of iterations) affects the resultant diagram in a hill-climbing layout system. Using an iterative process, we produce diagram layouts that are significantly better than those produced by ad-hoc parameter settings.

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