Enhancing the performance of watermarking based on Cat Swarm Optimization method

2011 ◽  
Author(s):  
G. Kalaiselvan ◽  
A. Lavanya ◽  
V Natrajan
Keyword(s):  
Optimization Method ◽  
Swarm Optimization ◽  
Cat Swarm Optimization

scholarly journals Cat Swarm Optimization to Shunt Capacitor Allocation in Algerian Radial Distribution Power System

2018 ◽  
Vol 7 (3) ◽  
pp. 143
Author(s):  
Amar Hamzi ◽  
Rachide Meziane
Keyword(s):  
Distribution Systems ◽  
Search Algorithm ◽  
Optimization Method ◽  
Algorithm Optimization ◽  
Swarm Optimization ◽  
Capacitor Switching ◽  
Cat Swarm Optimization ◽  
Capacitor Placement ◽  
Capacitor Allocation ◽  
Shunt Capacitor

This paper presents a Cat Swarm Optimization (CSO) Algorithm optimization method to shunt capacitor placement on distribution systems under capacitor switching constraints. The optimum capacitor allocation solution is found for the system of feeders fed through their transformer and not for any individual feeder. The main advantages due to capacitor installation, such as capacity release and reduction of overall power and energy losses are considered. The capacitor allocation constraints due to capacitor-switching transients are taken into account. These constraints are extremely important if pole-mounted capacitors are used together with station capacitor bank. Cat Swarm search algorithm is used as an optimization tool. An illustrative example for Algerian example is presented.

A Slow-Wave Structure Optimization with Variable Helix Section Length in STWT Based on CI-NMCSO Algorithm

2018 ◽  
Vol 17 (04) ◽  
pp. 1850020
Author(s):  
Huijuan Liu ◽  
Dongming Zhao ◽  
Kewen Xia
Keyword(s):  
Slow Wave ◽  
Structure Optimization ◽  
Wave Structure ◽  
Optimization Method ◽  
Slow Wave Structure ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Variable Helix ◽  
Better Than

A novel slow-wave structure optimization method on Chaos-improved Normal mutation cat swarm optimization (CI-NMCSO) algorithm is proposed. Under the variable helix section length in STWT, the CI-NMCSO combined with 1D CHRISTINE code is used to calculate the best set of pitch distribution and section length with the objective function of electron beam efficiency improvement. Quantum particle swarm optimization (QPSO) and Cauchy mutated cat swarm optimization (CMCSO) algorithms are applied to make performance comparison. Experimental results show that the beam efficiency has been increased by CI-NMCSO from rated value 30% to 45.3%, and the values using CMCSO and QPSO are 41.8% and 36.5%, respectively, the convergence speed of CI-NMCSO is the fastest, only 16 iterations, while CMCSO and QPSO take 19 and 23 iterations, so the performance of CI-NMCSO is better than CMCSO and QPSO on both optimization precision and calculation speed in terms of slow-wave structure optimization, and is also superior to that with equal section length when the helix section length is variable.

scholarly journals Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Lakshman Pappula ◽  
Debalina Ghosh
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Performance Metrics ◽  
Beam Width ◽  
Optimization Method ◽  
Binary Particle Swarm Optimization ◽  
Planar Antenna ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Cat Swarm Optimization

The process of thinned antenna array synthesis involves the optimization of a number of mutually conflicting parameters, such as peak sidelobe level, first null beam width, and number of active elements. This necessitates the development of a multiobjective optimization approach which will provide the best compromised solution based on the application at hand. In this paper, a novel multiobjective normal mutated binary cat swarm optimization (MO-NMBCSO) is developed and proposed for the synthesis of thinned planar antenna arrays. Through this method, a high degree of flexibility is introduced to the realm of thinned array design. A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multiobjective binary particle swarm optimization method (MO-BPSO). A comparative study is carried out to quantify the performance of the two algorithms using two performance metrics.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Sign in / Sign up

Export Citation Format

Share Document