CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES SYLLABUS FOR ME 3RD SEM CSE - Anna University Internal marks 2018

CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES SYLLABUS FOR ME 3RD SEM CSE

ANNA UNIVERSITY, CHENNAI
REGULATIONS - 2013
CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES SYLLABUS
ME 3RD SEM COMPUTER SCIENCE AND ENGINEERING SYLLABUS
CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES SYLLABUS
CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES SYLLABUS

OBJECTIVES:
 Learn fundamental principles of Multiobjective Optimization (MOP)
 Survey different Multiobjective Optimization algorithms
 Introduce various design issues of MOP
 Develop and Evaluate MOP Algorithms
 Learn Parallel and hybrid MOP Algorithms
 Learn other Metaheuristics

UNIT I INTRODUCTION AND CLASSICAL APPROACHES
Multiobjective optimization: Introduction - Multiobjective optimization problem-principles – Difference between single and multiobjective optimization – Dominance and Pareto Optimality , Classical Methods – Weighted Sum - Constraint method – Weighted Metric methods – Benson’s method - Value Function - Goal Programming methods – Interactive Methods

UNIT II MOP EVOLUTIONARY ALGORITHMS
Generic MOEA - Various MOEAs: MOGA, NSGA-II, NPGA, PAES, SPEA2, MOMGA, micro GA - Constrained MOEAs: Penalty Function approach - Constrained Tournament – Ray – Tai –Seow’s Method.

UNIT III THEORETICAL ISSUES
Fitness Landscapes - Fitness Functions - Pareto Ranking - Pareto Niching and Fitness Sharing - Recombination Operators - Mating Restriction - Solution Stability and Robustness - MOEA Complexity - MOEA Scalability - Running Time Analysis - MOEA Computational Cost - No Free Lunch Theorem.

UNIT IV MOEA TESTING, ANALYSIS, AND PARALLELIZATION
MOEA Experimental Measurements – MOEA Statistical Testing Approaches – MOEA Test Suites - MOEA Parallelization: Background – Paradigms – Issues - MOEA Local Search Techniques.

UNIT V APPLICATIONS AND ALTERNATIVE METAHEURISTICS
Scientific Applications: Computer Science and Computer Engineering - Alternative Metaheuristics: Simulated Annealing – Tabu Search and Scatter Search – Ant System – Distributed Reinforcement Learning – Particle Swarm Optimization – Differential Evolution – Artificial Immune Systems - Other Heuristics.

TOTAL:45 PERIODS

OUTCOMES:
Upon Completion of the course,students will be able to
 Explain MOP principles
 Explain classical methods to solve MOP problems
 Be familiar with and explain structures of different MOP algorithms
 Solve constrained MOP problems
 Explain various design issues of MOP algorithms
 Perform a evaluation and analysis of MOP algorithm results
 Explain parallelization of MOP algorithms
 Develop parallel and hybrid MOP algorithms
 Identify various real time MOP applications
 Explain other search algorithms

REFERENCES:
1. Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen, “Evolutionary
Algorithms for Solving Multi-objective Problems”, Second Edition, Springer, 2007.
2. Kalyanmoy Deb, “ Multi-Objective Optimization Using Evolutionary Algorithms”, John Wiley, 2002.
3. Aimin Zhoua, Bo-Yang Qub, Hui Li c, Shi-Zheng Zhaob, Ponnuthurai Nagaratnam Suganthan b, Qingfu Zhangd, “Multiobjective evolutionary algorithms: A survey of the state of
the art”, Swarm and Evolutionary Computation (2011) 32–49.
4. E Alba, M Tomassini, “Parallel and evolutionary algorithms”, Evolutionary Computation, IEEE Transactions on 6 (5), 443-462.
5. Crina Grosan, Ajith Abraham, “Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews”, Studies in Computational Intelligence, Vol. 75, Springer, 2007.
6. Christian Blum and Andrea Roli. 2003. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35, 3 (September 2003), 268-308.

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