An Introduction to Non-Traditional Optimization Algorithms: Part 1

I have decided to make some lecture videos on non traditional optimization. I have just made an introductiory lecture on conventional optimization algorithms.

The drawbacks of  conventional algorithms gives the motivation for non traditional algorithms like Genetic algorithms and its variants.
Part 1

Part 2

I will add more videos on this topic.
Thanks for reading.

Solving Economic Dispatch and Optimal Power Flow by GA

The economic dispatch problem is described as an act of minimizing the total fuel cost of the committed generators while satisfying the demand, network constraints. and plant.

Follow these steps.

Step II Solution Methodology

1. Choose a reference plant .For economic dispatch choose the plant with large capacity (range). In case of optimal power flow the slack bus is the reference bus.
2. For  both problems the number of  control(independent) variables to be determined in n-1.The reference plant  allocation is determined from the  constraint equations A2.

While solving the quadratic equation consider the positive solution. Check for the plant limits. If it is violating the limits allocate that particular limit.
3. Write a function file of n-1 control variables which return the total fuel cost fuel cost and the allocation. In case optimal power flow you have to use a power flow routine to determine the reference plant(slack bus) allocation.
4.The data file (fuel cost equations ,demand,loss coefficients,bus data,line data) and the gaoptions is put in a file  and the function file is run by the GA with options.


The program can be downloaded from the matlab cetral file excange

http://www.mathworks.de/matlabcentral/fileexchange/20825

Solving Optimization Problems Using MATLAB GA toolbox-Part 1

The GA tool box of MATLAB is good in solving hard optimization problems. It can be run form (i) GUI (Graphical  User Interface) mode or(ii) Command line Mode.


GA A Different Introduction

Genetic Algorithm or GA is one of th basic and powerful heuristic optimization algorithms.If you read any material on this algorithm you can observe the following points.

• 1.It is working on the population dynamics.
• 2.It is searching form a set of solutions(population) .
• 3.It does not need the derivative or continuous solution search space.
• 4.It doing some probabilistic operations like Reproduction,Cross Over,Mutation on the old  population to produce a new population.

MATLAB GA Toolbox

This toolbox contains some matlab files which can do the above described actions.This can be run in two modes.The GA toolbox is written as a minimization tool. Maximization problems also can be done by converting the Maximization problem as minimization problem.

1.GUI Mode

2.Command line Mode

Steps For Solving Optimization Problem 


To use the GA toolbox you need not know anything about Ga and its dynamics. All you should know is some fundamentals about optimization or operation research and basic matlab commands.


1. Model the optimization problem as a unconstrained  minimization problem.

2. Write the unconstrained minimization problem  as matlab function file.
3. Run the matalb ga tool box in a command line mode or GUI mode.




I am giving one example in GUI .It s problem of minimizing a  two variable quadratic function subject to a linear equality constraint..Just click on the figure to enlarge the figure.

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