CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES

CP7027      MULTI OBJECTIVE OPTIMIZATION TECHNIQUES

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.

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), 268308.