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Computational Intelligence For Optimization · PDF file Computational Intelligence For Optimization Deepak Sharma Research Fellow Department of Electrical and Computer Engineering

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  • Computational Intelligence For Optimization

    Deepak SharmaDeepak Sharma Research Fellow

    Department of Electrical and Computer Engineering National University of Singapore


  • 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

    2 SIS@SMU

  • SIS@SMU3

    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


  • Procedure Initialize Procedure Initialize Population

    Fitness evaluation of individuals

     Population Based Meta-Heuristic Algorithm  Initial random population of candidate




    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





    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



    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


     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:


     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


  • 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)


  • Customized Genetic AlgorithmCustomized Genetic Algorithm  Structure


     Domain specific initial population


  • 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


     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


  • 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


  • 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


  • Contd.Contd.  Shapes of structures


  • 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


     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


    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


  • 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


    ( 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 4 8 12 16 20 24Lo ad

    D em

    an d

    generator can not start or shut-down, immediately.

     Thus

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