scholarly journals An Adaptive Embedding Strength Watermarking Algorithm Based on Shearlets’ Capture Directional Features

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1377
Author(s):  
Qiumei Zheng ◽  
Nan Liu ◽  
Fenghua Wang
Keyword(s):  
Artificial Bee Colony ◽  
Discrete Wavelet ◽  
Geometric Attacks ◽  
Bee Colony ◽  
Adaptive Embedding ◽  
Strong Robustness ◽  
Simulation Results ◽  
Fixed Embedding ◽  
False Positive Problem ◽  
Made In

The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity.

scholarly journals Dynamic Relay Satellite Scheduling Based on ABC-TOPSIS Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shufeng Zhuang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Xiaoguang Chen
Keyword(s):  
Artificial Bee Colony ◽  
Dynamic Scheduling ◽  
Research Field ◽  
Scheduling Problem ◽  
Ideal Solution ◽  
Bee Colony ◽  
Order Preference ◽  
Simulation Results ◽  
Satellite Scheduling ◽  
Selection Of

Tracking and Data Relay Satellite System (TDRSS) is a space-based telemetry, tracking, and command system, which represents a research field of the international communication. The issue of the dynamic relay satellite scheduling, which focuses on assigning time resource to user tasks, has been an important concern in the TDRSS system. In this paper, the focus of study is on the dynamic relay satellite scheduling, whose detailed process consists of two steps: the initial relay satellite scheduling and the selection of dynamic scheduling schemes. To solve the dynamic scheduling problem, a new scheduling algorithm ABC-TOPSIS is proposed, which combines artificial bee colony (ABC) and technique for order preference by similarity to ideal solution (TOPSIS). The artificial bee colony algorithm is performed to solve the initial relay satellite scheduling. In addition, the technique for order preference by similarity to ideal solution is adopted for the selection of dynamic scheduling schemes. Plenty of simulation results are presented. The simulation results demonstrate that the proposed method provides better performance in solving the dynamic relay satellite scheduling problem in the TDRSS system.

scholarly journals Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5253
Author(s):  
Fthi M. Albkosh ◽  
Muhammad Suzuri Hitam ◽  
Wan Nural Jawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Rozniza Ali
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Texture Classification ◽  
Optimization Technique ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Bee Colony ◽  
Selection Of

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms

2019 ◽  
Vol 28 (01) ◽  
pp. 1950004 ◽  
Author(s):  
Dervis Karaboga ◽  
Beyza Gorkemli
Keyword(s):  
Artificial Bee Colony ◽  
Standard Form ◽  
Optimization Methods ◽  
Traveling Salesman ◽  
The Other ◽  
Optimization Approach ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Convergence Performance ◽  
Simulation Results

Artificial bee colony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.

scholarly journals REAL POWER LOSS REDUCTION ENHANCED ARTIFICIAL BEE COLONY ALGORITHM

2018 ◽  
Vol 6 (3) ◽  
pp. 203-213
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Search Space ◽  
Loss Reduction ◽  
Real Power ◽  
Bee Colony ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Enhanced Artificial Bee Colony (EABC) algorithm is proposed for solving optimal reactive power problem. The projected method assimilates crossover operation from Genetic Algorithm (GA) with artificial bee colony (ABC) algorithm. The EABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space.  Projected EABC algorithm has been tested on has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm in reducing the real power loss.

Mechanical Arm Active Disturbance Rejection Control Based on Artificial Bee Colony Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1511-1514 ◽  
Author(s):  
Xiao Li Liu ◽  
Liang Peng Xiong
Keyword(s):  
Honey Bees ◽  
Disturbance Rejection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Simple Algorithm ◽  
Bee Colony ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Simulation Results ◽  
Swarm Intelligence Algorithm

The mechanical arm is a multi-input multi-output, highly non-linear, strong coupling complex system. Active Disturbance Rejection Controller is of good performance and relatively simple algorithm. Artificial Bee Colony algorithm is a new swarm intelligence algorithm based on the behavior of honey bees. The linear ADRC was adopted to realize the moving control of the mechanical arm and the controller parameters were chosen and optimized by ABC algorithm. Simulation results are satisfied and well prove the feasibility and validity of the algorithm.

scholarly journals A Simple and Efficient Artificial Bee Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yunfeng Xu ◽  
Ping Fan ◽  
Ling Yuan
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Population Based ◽  
Search Pattern ◽  
Stochastic Algorithms ◽  
Search Process ◽  
Stochastic Algorithm ◽  
Bee Colony ◽  
Simulation Results

Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

scholarly journals Artificial Bee Colony algorithm used for load balancing in cloud computing: Review

2019 ◽  
Vol 8 (2) ◽  
pp. 156
Author(s):  
ARIF Ullah
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Artificial Bee Colony ◽  
Mathematical Technique ◽  
Abc Algorithm ◽  
Survey Paper ◽  
Bee Colony ◽  
Strong Robustness ◽  
High Flexibility ◽  
Comprehensive Study

<p>Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed workload among various nodes equally in a situation where some nodes are under load and some are overload.  Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005.  It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This survey paper is a comprehensive study about load balancing in cloud computing using the ABC algorithm. It also defines some basic concept about swarm intelligent and its property<strong>.</strong></p>

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