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Presentation Outline
•Introduction
•Literature Overview
•Constraints
•Assumptions and Dependencies
•Specific Requirements
•Project Management
•Conclusion
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Multi-Objective Optimization with Genetic Algorithms: A Comprehensive Literature Review, Slides of Applications of Computer Sciences
An in-depth literature review on multi-objective optimization problems and the application of genetic algorithms to find optimal solutions. It covers the differences between single-objective and multi-objective optimization, the advantages and working of genetic algorithms, various techniques for multi-objective evolutionary algorithms (moeas), and performance metrics and benchmark test problems.
Typology: Slides
2011/2012
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Presentation Outline
• Introduction
• Literature Overview
• Constraints
• Assumptions and Dependencies
• Specific Requirements
• Project Management
• Conclusion
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Introduction
• Objectives
- To develop a toolbox for solving multi- objective optimization problems with the help of genetic algorithms
• Deliverables
- GUI for solving MOP
- Toolbox for MOP
- Complete project report
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- Evolutionary Computation
- Subfield of Artificial Intelligence
- Involves Evolutionary Algorithms
- Genetic Algorithms (GA)
- Fast growing optimization technique
- Used to find true and approximate solutions.
- Reason for Success of GA
- Broad applicability
- Ease of use docsity.com
- Advantages of GA
- Flexibility
- Solve complex problems
- Trouble-free to apply
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- Multi-objective Optimization
Problem (MOP)
- Involves more then one objective functions
- General form is:
( ) ( )
m j k L U i i i
Minimize Maximize f x m M
subject to g x j J
h x k K
x x x
i 1, 2,......., ; n
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- Convex Function
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- Difference Between SOP and MOP
- Two goals instead of one
- Dealing with two search spaces
- No artificial fix-ups
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- Example
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- Pareto-optimality
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- Locally and Globally Pareto-optimal
Solutions
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- Applications
- Optimization Problems in Mobile Communications - The base station location with frequency assignment - The OVSF code assignment problem - The joint base station scheduling problem
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- Elitist MOEAs
- Rudolph’s Elitist MOEA
- Elitist Non-dominated Sorting GA
- Distance-Based Pareto GA
- Strength Pareto EA
- Thermodynamical GA
- Pareto-Archived Evolution Strategy
- Multi-Objective Messy GA
- Constrained MOEAs
Jimenez-Verdegy-Gomez-Skarmeta’s Method
- Constrained tournament Method
- Ray-Tai-Seow’s Method docsity.com
- Performance Metrics
- Metrics Evaluating Closeness to the Pareto-optimal Front
- Metrics Evaluating diversity Among Non-dominated Solution
- Metrics Evaluating Closeness and diversity
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