Course Name: Traditional and Non-Traditional Optimization Tools

Course abstract

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.


Course Instructor

Media Object

Prof. Dilip Kumar Pratihar

I received BE (Hons.) and M. Tech. from REC (NIT) Durgapur, India, in 1988 and 1994, respectively. I obtained my Ph.D. from IIT Kanpur, India, in 2000. I received University Gold Medal, A.M. Das Memorial Medal, Institution of Engineers’ (I) Medal, and others. I completed my post-doctoral studies in Japan and then, in Germany under the Alexander von Humboldt Fellowship Programme. I received Shastri Fellowship (Indo-Canadian) in 2019 and INSA Teachers’ Award 2020. I am working now as a Professor (HAG scale) of IIT Kharagpur, India. My research areas include robotics, soft computing and manufacturing science. I have published more than 275 papers and book-chapters. I have written the textbooks on “Soft Computing” and “Fundamentals of Robotics”, co-authored another textbook on “Analytical Engineering Mechanics”, edited a book on “Intelligent and Autonomous Systems”, co-authored reference books on “Modeling and Analysis of Six- legged Robots”; “Modeling and Simulations of Robotic Systems Using Soft Computing”; “Modeling and Analysis of Laser Metal Forming Processes by Finite Element and Soft Computing Methods” and “Multibody Dynamic Modeling of Multi-legged Robots”. My textbook on “Soft Computing” had been translated into Chinese language in 2009. I have guided 22 Ph.D.s. I am in editorial board of 10 International Journals. I have been elected as FIE, MASME and SMIEEE. I have completed a few sponsored (funded by DST, DAE, MHRD) and consultancy projects. I have filed two patents.
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Teaching Assistant(s)

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 Course Duration : Jan-Mar 2021

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 Syllabus

 Enrollment : 18-Nov-2020 to 25-Jan-2021

 Exam registration : 15-Jan-2021 to 12-Feb-2021

 Exam Date : 21-Mar-2021

Enrolled

408

Registered

21

Certificate Eligible

17

Certified Category Count

Gold

2

Silver

11

Elite

3

Successfully completed

1

Participation

0

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Traditional and Non-Traditional Optimization Tools - Toppers list

JINKA RANGANAYAKULU 96%

R V COLLEGE OF ENGINEERING

N KAMALA 91%

JAIN (DEEMED-TO-BE UNIVERSITY)

Enrollment Statistics

Total Enrollment: 408

Registration Statistics

Total Registration : 21

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.