scholarly journals A Robust Time Efficient Watermarking Technique for Stereo Images

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
M. A. Abdou ◽  
A. A. Saleh
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Complexity ◽  
Real World ◽  
Copyright Protection ◽  
Imaging Techniques ◽  
Smart Phones ◽  
Computation Method ◽  
Wavelet Domain ◽  
Stereo Images ◽  
Swarm Optimization

Stereoscopic and multiview imaging techniques are used for reproducing a natural or real world scene. However, the fact that more information is displayed requires supporting technologies to ensure the storage and transmission of the sequences. Beyond these supports comes watermarking as a desirable alternative solution for copyright protection of stereo images and videos. This paper introduces a watermarking method applied to stereo images in wavelet domain. This method uses a particle swarm optimization (PSO) evolutionary computation method. The aim is to solve computational complexity problems as well as satisfy an execution time that complies with normal PCs or smart phones processors. Robustness against image attacks is tested, and results are shown.

scholarly journals Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping

2021 ◽  
Vol 11 (18) ◽  
pp. 8634
Author(s):  
Ammar Alammari ◽  
Ammar Ahmed Alkahtani ◽  
Mohd Riduan Ahmad ◽  
Ahmed Aljanad ◽  
Fuad Noman ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Cross Correlation ◽  
Particle Swarm ◽  
Sliding Window ◽  
Event Extraction ◽  
Wavelet Domain ◽  
Estimation Errors ◽  
Swarm Optimization ◽  
Lightning Strikes ◽  
The Real

Several processing methods have been proposed for estimating the real pattern of the temporal location and spatial map of the lightning strikes. However, due to the complexity of lightning signals, providing accurate lightning maps estimation remains a challenging task. This paper presents a cross-correlation wavelet-domain-based particle swarm optimization (CCWD-PSO) technique for an accurate and robust representation of lightning mapping. The CCWD method provides an initial estimate of the lightning map, while the PSO attempts to optimize the trajectory of the lightning map by finding the optimal sliding window of the cross-correlation. The technique was further enhanced through the introduction of a novel lightning event extraction method that enables faster processing of the lightning mapping. The CCWD-PSO method was validated and verified using three narrow bipolar events (NBEs) flashes. The observed results demonstrate that this technique offers high accuracy in representing the real lightning mapping with low estimation errors.

Multi-objective particle swarm optimization using Pareto-based set and aggregation approach

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740073 ◽  
Author(s):  
Song Huang ◽  
Yan Wang ◽  
Zhicheng Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Multi Objective

Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.

A Survey to Nature Inspired Soft Computing

2017 ◽  
Vol 8 (2) ◽  
pp. 112-133 ◽  
Author(s):  
Deepak Kumar ◽  
Sushil Kumar ◽  
Rohit Bansal ◽  
Parveen Singla
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Real World ◽  
Firefly Algorithm ◽  
Travelling Salesman Problem ◽  
Cuckoo Search ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Physical Chemical ◽  
Time Problem

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

scholarly journals Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

2017 ◽  
Author(s):  
Adithya Sagar ◽  
Rachel LeCover ◽  
Christine Shoemaker ◽  
Jeffrey Varner
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Coagulation Cascade ◽  
Benchmark Problems ◽  
Test Functions ◽  
Swarm Optimization ◽  
Particle Swarms

AbstractBackgroundMathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.ResultsWe tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed trials with function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade.ConclusionsDOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org.

An Overview of Mutation Strategies in Particle Swarm Optimization

2020 ◽  
Vol 11 (4) ◽  
pp. 16-37
Author(s):  
Waqas Haider Bangyal ◽  
Jamil Ahmad ◽  
Hafiz Tayyab Rauf
Keyword(s):  
Particle Swarm Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Comprehensive Overview ◽  
Swarm Optimization ◽  
Numerical Experimentation ◽  
Mutation Strategies ◽  
Real World Problems

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

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