Computational Intelligence For
Optimization
Deepak SharmaDeepak Sharma
Research Fellow
Department of Electrical and Computer Engineering
National University of Singapore
Email: eledeepa@nus.edu.sg
Overview of TalkOverview of Talk
Genetic Algorithm for Designing
Introduction and Procedure of GA Introduction and Procedure of GA
Application in designing mechanism
Approach
Results and Discussion
Conclusions
M l i A M d li f h d li Multi-Agent Modeling for scheduling
Introduction of MAS and Thermal generation scheduling
Problem formulation Problem formulation
Approach
Results and Discussion
Conclusions
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Borrowed from the lecture notes of
Dr Dipti Srinivasan @ NUS
Genetic Algorithm for DesigningGenetic Algorithm for Designing
Introduction
Computerized search and optimization technique Computerized search and optimization technique
Based on Darwin’s principal of natural selection and genetics
inheritance
Works on the population
Does not require gradient information
Can avoid premature convergence to local optima's Can avoid premature convergence to local optima s
Suitable for complex problems
Example: Mixed variables, continuous or discontinuous search space,
non-linearity, multiple objectives etc.
However, requires more computation time than point-by-point
search methods
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Procedure Initialize Procedure Initialize
Population
Fitness
evaluation of
individuals
Population Based Meta-Heuristic Algorithm
Initial random population of candidate
solutions
Ranking
Termination
Parent
Population
so ut o s
Fitness evaluation of individuals, e.g., objective
and constraint violation calculations
Ranking: Assign rank to every individual in the
population based on fitness values Termination
criterion
met?
SelectionElimination
No
Yes
ppopulation based on fitness values
Selection: Survival of fittest by choosing good
solutions based on ranking
Crossover: Create new individuals from the
selected parents
Terminate
selected parents
Mutation: Perturb the newly created individuals
Fitness evaluation of new population
Ranking of new population Report
results
CrossoverRanking Elimination to propagate good individuals
Termination criterion
Report results
results
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MutationFitness evaluation
Child
Population
Application in DesigningApplication in Designing
Goal: To design a mechanism
Path generating compliant mechanismg g p
One piece flexible elastic mechanism that has
to generate user defined path
Identify the locations where y
material has to be filled optimally
Advantage over traditional mechanism
Joint-less and monolithic structureJ
Less friction wear and noise
Light weight
Easy to manufacture without assemblyasy to a u actu e w t out asse b y
Applications
Micro-actuators
Micro switches
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Micro-switches
Designing criterion in literature
Minimizing the gap between actual and user
defined paths (single or multi objective)defined paths (single or multi-objective)
Limitation: Without limiting the gap, actual
and prescribed paths can be apart from
each other
Motivation
Functionality of mechanism should be represented in terms of
constraints so that every feasible solution can be referred as path
generating compliant mechanism
Can solve the problem for optimal distribution of material in the
design domain
P d A h Proposed Approach
Objective:
Minimum weight
Constraints:
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Constraints:
Characteristics of given designing problemg g g p
•Discrete search space
•Mixed-variables
•Non-linear
•C t i d ti i ti bl•Constrained optimization problem
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Multi-ObjectivizationMulti Objectivization
Definition:
Provide solution to single objective optimization by adding helper g j p y g p
objective(s) and deal with the problem as multi-objective optimization
Advantages
G id h h f l i l i h id l l i Guide the search for evolutionary algorithms to avoid local optima
Maintain the diversity in the population
Further Benefit
Multiple optimal solutions
Bi-Objectives:
Minimize weight of structure (Primary objective)
Minimize supplied input energy to structure (Secondary Objective)
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Customized Genetic AlgorithmCustomized Genetic Algorithm
Structure
representationrepresentation
Domain specific initial
population
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Contd.Contd.
Chromosome
Shape
C diti
Connectivity and repairing
techniques
To connect topology Conditions To connect topology
Single point connectivity
Floating element
2D crossover operator
Finite element analysis
S d d fl i l i Stress and deflection analysis
Parallel Computing
Master-Slave architecture
F d i th t ti
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For reducing the computation
time
Contd.Contd.
Local search: Post processing
Mutation and hill-climbing Mutation and hill climbing
Weighted sum method to reduce the bi-objective problem into
single objective
Weights Weights
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Results and DiscussionResults and Discussion
Comparison
Customized Initialization of structure shapes Customized Initialization of structure shapes
Random Initialization of structure shapes
However, rest of the customized GA is same for both cases
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Contd.Contd.
Performance test
R-indicator: Closeness to R indicator: Closeness to
the reference Pareto-Optimal
front
H l i di S d Hypervolume-indicator: Spread
and closeness
Path traced by solutions
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Contd.Contd.
Shapes of structures
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Contd.Contd.
Optimum conditions Main Findings
Formulation that can
guarantee feasible designs
Multiple solutions for
decision making
Computation time
ec s o a g
Optimum set of conditions
Better performance of
customized GA against customized GA against
random initialization based
GA
Customized schemes able Customized schemes able
to generate practically
feasible designs
P ll l t i d GA
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Parallel customized GA
Multi-Agent Modeling for SchedulingMulti Agent Modeling for Scheduling
Multi-Agent System (MAS)
System of flexible autonomous agents where these agents are loosely connected
and act in the environment to achieve their goals
Broadly classified approaches for exploiting MAS
Building flexible-robust-extensible system
Modeling the large and complex system
S b fi Some benefits
Increase computation speed because of parallel computation and asynchronous
operations
Graceful degradation of system when one or more agents failg y g
Scalability and flexibility of adding and removing agents
Reusability of agents
Critical challenges
U bl b h i i h i i Unstable behavior in changing environment
Converge to sub-optimal solution due to limited visibility
Action or decision taken by an agent may not be suitable for another agent
which can be reduced by sharing information. But the problem is when to
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y g p
communicate and to which agent
Difficult in debugging and testing parallel and distributed system
Day Ahead Thermal Generation SchedulingDay Ahead Thermal Generation Scheduling
In restructured power system
Generator companies (GENCOs) are independent and Generator companies (GENCOs) are independent and
autonomous
These companies compete with each other for selling the power and
reserve in the power market to earn profitreserve in the power market to earn profit
Aim: To maximize their profit by generating desired amount of power
and reserve by optimally scheduling the thermal generators
I bl f GENCO h l b dd k It is important problem for GENCO while bidding in power market
GENCO performs a day ahead scheduling task based on the
forecast data
We assume that the forecast data is given
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Contd.Contd.
Scheduling involves two decision making processes
ON/OFF status of every generator (Unit Commitment) ON/OFF status of every generator (Unit Commitment)
In peak load hours, more number of units
should be committed that can earn profit
I ff k l d h i t it
1500
2000
(
M
W
)
In off-peak load hours, appropriate units
should be committed that can earn profit
Because of the physical constraints, any
t t t t h t d
0
500
1000
0 4 8 12 16 20 24Lo
ad
D
em
an
d
generator can not start or shut-down,
immediately.
Thus
