P. Surekha

Genetic Algorithm and Particle Swarm Optimization approaches to solve combinatorial job shop scheduling problems

In this paper an eminent approach based on the paradigms of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, fuzzy logic is applied for planning and then scheduling is optimized using evolutionary computing algorithms such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The well known Adams, Balas, and Zawack 10 × 10 instance (ABZ10) problem is selected as the experimental benchmark problem and simulated using MATLAB R2008b. The results of the optimization techniques are compared with the parameters like makespan, waiting time, completion time and elapse time. The performance evaluation of optimization techniques are analysed and the superior evolutionary technique for solving job shop scheduling problem is determined.

A Self-Adaptive Fuzzy C-means based Radial Basis Function Network to Solve Economic Load Dispatch Problems

decades, with a large increase in power demand, fuel cost, and limited fuel supply it has become very essential to run the power systems with minimum cost so that the committed units serve the expected load demand. The basic objective of Economic Load Dispatch (ELD) is to distribute the total generation among the generation units in operation, in order to meet the load demand at minimum operating cost while satisfying the system equality and inequality constraints. Nature inspired computing techniques like Artificial Neural Networks (ANN) are preferred for solving ELD problems because they do not impose any restrictions on the shape of the fuel cost curve and are capable of providing good solution quality, and higher precision solutions very close to the global optimum. In this paper, the application of Fuzzy c*means based Radial Basis Function Network (RBFN) to ELD is proposed in order to minimize the error function through a self adaptive process until the error is less than a given tolerance leading to a best solution. The applicability and viability for practical applications has been tested on two different power systems, viz., a IEEE 30 bus 6 unit test system and a 20 unit test system and the experiments were carried out on MATLAB R2008b software. Comparison of the results with the conventional Lambda Iteration method demonstrates the effectiveness of RBFN in solving ELD problems based on fuel cost, power loss, total generated power, algorithmic efficiency, and computational time.

Wind Energy Conversion Systems

On completion of this chapter, the reader will have knowledge on: Characteristics of Wind Energy. Basic components of Wind Energy Conversion systems. Types of Wind Turbine Generator Configurations. Power Converter Topologies for Wind Turbine Generator. MATLAB/SIMULINK model of Wind Turbine. MATLAB/SIMULINK model of different types of turbines in WECS. Grid Integration and MATLAB/SIMULINK model of Grid Connected WECS.

Application of MATLAB/SIMULINK in Solar PV Systems

On completion of this chapter, the reader will have knowledge on: Basic components of Solar PV system and its merits and demerits. Involvement of power electronic devices in Solar PV components. MATLAB/SIMULINK model of different control strategies of power conditioning unit. Importance of MATLAB/SIMULINK model in improving the efficiency of the overall solar PV system. Characteristics of Solar PV panel and its MATLAB/SIMULINK model. Characteristics and MATLAB/SIMULINK model of Solar PV power conditioning unit.

PERFORMANCE COMPARISON OF OPTIMIZATION TECHNIQUES ON ROBUST DIGITAL-IMAGE WATERMARKING, AGAINST ATTACKS

Increasing illegal exploitation and imitation of digital images in the field of image processing has led to the urgent development of copyright protection methods. Digital watermarking has proved to be the most effectivemethod for protecting illegal authentication of data. In this article, we propose a hybrid digital-image watermarking scheme based on computational intelligence paradigms such as a genetic algorithm (GA) and particle swarm optimization (PSO). The watermark image is embedded into the host image using discrete wavelet transform (DWT). During the extraction process, GA, PSO, and the hybrid combination of GA and PSO are applied to improve the robustness and fidelity of the watermarked image by evaluating the fitness function. The perceptual transparency and the robustness of both the watermarked and the extracted images is evaluated by applying filtering attacks, additive noise, rotation, scaling, and JPEG compression attacks to the watermarked image. From the simulation results, the performance of the hybrid particle swarm optimization technique is proved best, based on the computed robustness and transparency measures, as well as the evaluated parameters of elapsed time, computation time, and fitness value. The performance of the proposed scheme was evaluated with a set of 50 textures images taken from online resources of Tampere University of Technology, Finland, and the entire algorithm for different stages was simulated using MATLAB R2008b.

A methodology to schedule and optimize job shop scheduling using computational intelligence paradigms

Evolutionary computation is emerging as a novel engineering computational paradigm, which plays a significant role in several optimization problems. Job-shop scheduling problem (JSSP) is one among the common NP-hard combinatorial optimization problems. The JSSP is defined as allocation of machines for a set of jobs over time in order to optimize the performance measure satisfying certain constraints like processing time, waiting time, completion time, etc. In this paper an eminent approach based on the paradigms of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, the jobs are scheduled, in which the machines and jobs with respect to levels are planned. Scheduling is optimized using evolutionary computing algorithm such as Genetic Algorithm (GA), which is a powerful search technique, built on a model of the biological evolution. Like natural evolution GA deal with a population of individuals rather than a single solution and fuzzy interface is applied for planning and scheduling of jobs. The well known Fisher and Thompson 10×10 instance (FT10) problem is selected as the experiment problem. The discussion on the proposed techniques and paths of future research are summarized.

Soft Computing Techniques in Solar PV

On completion of this chapter, the reader will have knowledge on: Importance of soft computing techniques such as neural networks, fuzzy logic and genetic algorithms in solar PV system. Soft computing techniques used in MPPT of Solar PV system and its MATLAB/SIMULINK model. Prediction of solar irradiance using soft computing techniques. Parameter estimation of Solar PV module using Genetic Algorithms.

Soft Computing Techniques in Wind Energy Conversion Systems

On completion of this chapter, the reader will have knowledge on: Importance of soft computing techniques in WECS. Prediction of power factor using soft computing techniques. Fuzzy based Pitch angle control. Soft computing based MPPT in WECS. Economic dispatch of WECS using soft computing algorithms.

Hybrid Energy Systems

On completion of this chapter, the reader will have knowledge on: Basic knowledge on hybridizing solar PV module with wind energy system and diesel system MATLAB/SIMULINK model of hybrid solar PV and wind energy conversion system Converters used for hybrid solar PV and wind energy conversion system Role of intelligent multi agent system in HPS for energy management Fuzzy logic controller for hybrid power systems

Grid Integration Techniques in Renewable Energy Systems

On completion of this chapter, the reader will have knowledge on Grid Issues in integrating renewable energy systems. Converters used for grid integration techniques and its control strategy. MATLAB/SIMULINK models of Synchronous Reference Frame PLL (dq PLL), Stationary Reference Frame PLL (αβ PLL), Decoupled Synchronous Reference Frame PLL (DSRF PLL), Decoupled Stationary Reference frame PLL (Dαβ PLL) and Hybrid Dαβ PLL.. Filters used for grid integration techniques and its control strategy.