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Indian Institute of Technology, Kharagpur

Traditional and Non-Traditional Optimization Tools

Indian Institute of Technology, Kharagpur and NPTEL via Swayam

Overview

At the beginning of this course, a brief introduction will be given to optimization. The principle of optimization will be explained in detail. The working principles of some traditional tools of optimization, namely exhaustive search method, random walk method, steepest descent method will be discussed with suitable numerical examples. The drawbacks of traditional tools for optimization will be stated. The working principle of one of the most popular non-traditional tools for optimization, namely genetic algorithm (GA) will be explained in detailed. Schema theorem of binary-coded GA will be discussed. The methods of constraints handling used in the GA will be explained. The merits and demerits of the GA will be stated. The working principles of some specialized GAs, such as real-coded GA, micro-GA, visualized interactive GA, scheduling GA will be discussed with suitable examples. The principles of some other non-traditional tools for optimization, such as simulated annealing, particle swarm optimization will be explained in detail. After providing a brief introduction to multi-objective optimization, the working principles of some of its approaches, namely weighted sum approach, goal programming, vector-evaluated GA (VEGA), distance-based Pareto-GA (DPGA), non-dominated sorting GA (NSGA) will be explained with the help of numerical examples.
INTENDED AUDIENCE: : Students belonging to all disciplines of Engineering and Science and practicing Engineers can take this course
INDUSTRY SUPPORT :
  • RDCIS, Ranchi
  • CMERI, Durgapur, and others

Syllabus

COURSE LAYOUT

Week 1: Principle of Optimization; Traditional Methods of Optimization; Binary-Coded Genetic Algorithm (BCGA)Week 2: Binary-Coded Genetic Algorithm (BCGA) (contd.); Schema Theorem of BCGA; Constraints Handling; Real-Coded GAWeek 3: Faster Genetic Algorithms; Scheduling GAWeek 4: Scheduling GA (contd.); Simulated Annealing; Particle Swarm OptimizationWeek 5: Multi-Objective Optimization; Intelligent Optimization ToolWeek 6: A Practical Optimization Problem solved using different Traditional and Non-Traditional Optimization ToolsWeek 7: Solutions of a Practical Optimization Problem (contd.); Genetic Algorithm as Evolution ToolWeek 8: Genetic Algorithm as Evolution Tool (contd.); Summary of the Course

Taught by

Dilip Kumar Pratihar

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