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Training on
Searching for Optimal Solution
with Traditional & AI Optimization Techniques
Klang Valley
11 & 12 OCT, 2018 



" Defining and solving optimization programs
and monitoring solution progress..." 
Optimization can be broadly defined as the process of selecting a set of best parameters from some available alternatives subjecting to stated constraints and meeting specific objectives. Generally, the optimization process involves maximizing or minimizing a real mathematic function by systematically choosing input values from within an allowed set and computing the value of the function. Due to its nature, optimization has found broad usage in engineering and science. For example in automotive industry, optimization can be used to determine the best drivetrain settings to minimize emissions. On the other hand in financial sector, optimization can be used to determine the best investment options to minimize risks. In general, optimization process can be implemented and solved by using both traditional methods and artificialintelligence (AI) techniques such as genetic algorithm (GA).






This course is intended as a practical introduction to solving optimization problems using traditional optimization techniques and genetic algorithm. As such, there will be a series of handson exercises which are generally aimed to help translate the problems into practical solving using MATLAB.
What you will learn
 Identify the problem and formulate an objective function to solve it
 Understand various optimization solvers and choose appropriate optimization solver to solve the problem
 Understand the basic concepts of genetic algorithm and its implementation for solving optimization problem


Course methodology
The course begins with an overview of optimization fundamentals, guidance on writing objective functions, expressing constraints, choosing solvers to basic concepts of genetic algorithm. The course will be wellsupported by MATLAB examples.
Course structure
Day 1 – Traditional Optimization Techniques
 Introduction
 Concepts of optimization
 The optimization process
 Choice of design variables
 Formulation of objective function
 Limits and Bounds of design variables
 Formulation of constraints (linear and nonlinear)
 Using Optimization Tool
 Minimizing a simple mathematical function
 Introduction to solvers
 Linear programming
 Quadratic programming
 Binary integer programming
 Iterativebased method
 Leastsquares method
 Tips on choosing the solvers
 Case studies:
 Product design: finding the right parameters
 Communication – finding the optimal positions
Day 2 – Genetic Algorithm
 Introduction
 Genetic representation of numbers
 Binary representation
 Floatingpoint representation
 Processes of genetic computation
 Creation of initial population
 Fitness scoring
 Fitness scaling
 Selection of parent
 Genetic operators (elite, crossover, mutation)
 Using Optimization Tool
 Minimizing a complex mathematical function
 Case studies:
 Using GA for:
 Unconstraint optimization problem
 Linearly constraint optimization problem
 Nonlinear constraint optimization problem


Who should attend
Engineers and scientists at all levels who wish to learn about solving optimization problems with MATLAB. No background in optimization theory is, however, assumed. The detailed course material and many source code listings will be invaluable for both learning and reference.
Prerequisites
A basic knowledge of linear algebra and
familiarity with MATLAB working
environment.
Note: Due to the nature of the course and the learning expectations, the availability seats are limited. You need to register early to obtain confirmation of your space. 
11 & 12 OCT, 2018 @ MY
Venue : Klang Valley
10 % Early Registration Discount before 11 SEP
2018
10 % Group Discount for 3 or more from same organization

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