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Training on
Swarm Intelligence for Optimization


Klang Valley
10 & 11 JAN 2018
" the process of obtaining the best set of parameters to meet the design objectives within certain constraints..."

Optimization involves the process of obtaining the best set of parameters to meet the design objectives within certain constraints. This can be achieved by either using deterministic algorithms or applying stochastic methods. The latter includes evolutionary algorithms and swarm intelligence which have gained popularity in recent years. Swarm intelligence applies the concept and behaviors of creatures in nature in the way they search for food or try to survive difficult environment. Particle swarm optimization, in particular, mimics the movements of birds and fishes where they cooperate and share critical information among them in search of food. In engineering applications, the particle swarm has been formulated into a search algorithm in order to find the optimal values which can either minimize or maximize the objective function.

 
 

Course highlights

This course is a practical and non-mathematical approach to solving optimization problems using swarm intelligence method in general and particle swarm optimization in particular. By doing the exercises using MATLAB and PSO toolbox, participants will gain insights into the search principles and parameter tuning of the swarm optimization algorithms.

What you will learn

  • Understand the basic concept of stochastic search and swarm intelligence in single and multi-objective optimization problems
  • The structure of MATLAB coding for particle swarm optimization algorithms in engineering applications
  • Various parameter settings of particle swarms for efficient optimal search

Course methodology

The course begins with an overview of optimization problems and computational intelligence. The swarm intelligence methods are introduced with exercises in MATLAB coding. Detailed parameter tuning is covered using PSO toolbox. Some applications including multi-objective and engineering optimizations are demostrated.

Course duration

2 days

Course structure

Day 1 – Swarm Intelligence

  • Optimization
    • Single and Multi-Objectives Optimization Problems and Examples
    • Benchmark Test Functions for Algorithm Evaluation
    • Existing optimization techniques and approaches
  • Swarm Intelligence
    • Computational Intelligence Review
    • Particle Swarm Optimization (PSO):Variants/Parameters/ Properties
    • Ant Colony Optimization (ACO) and Other Nature/Bio-Inspired Algorithms
  • Writing Matlab Codes
    • Swarm Search Concept
    • Structure of Swarm Algorithms
    • Writing PSO Algorithms
    • Parameter Setting and Fine-Tuning
    • Performance Evaluation on Simple Test Functions

Day 2 – Engineering Optimization

  • PSO Toolbox
    • Exploring Features and Properties
    • Parameter Selection
    • Performance on Standard Test Functions
  • Multi-Objective Optimization
    • Multi-Objective PSO
    • Multi-Objective Genetic Algorithms (MOGA)
    • Performance on Test Functions
  • Applications
    • Engineering Systems Optimization
    • System Identification and Modelling
    • Model Validation and Performance Evaluation
  • Research Areas and Projects
    • Possible research areas or projects for academics and postgraduate students
    • Possible research areas or projects for industries







Who should attend

Academics, research students and engineers at all levels who wish to learn about solving engineering optimization using swarm intelligence method. Students and researchers involve in engineering design, system identification and modelling will find the course invaluable in their works

Prerequisites
Basic MATLAB programming.

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.

10 & 11 JAN, 2018 @ MY

Venue : Klang Valley
10 % Early Registration Discount Extended to 10 DEC 2017
10 % Group Discount for 3 or more from same organization

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