Multiobjective optimization for dynamic environments

This paper investigates the use of evolutionary multi-objective optimization methods (EMOs) for solving single-objective optimization problems in dynamic environments. A number of authors proposed the use of EMOs for maintaining diversity in a single objective optimization task, where they transform...

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Bibliographic Details
Main Authors: Bui, L.T., Abbass, H.A., Branke, J.
Format: Conference Proceeding
Language:English
Subjects:
Australia
Benchmark testing
Biological cells
Evolutionary computation
Genetic algorithms
Optimization methods
Portfolios
Robots
Sorting
Stock markets
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Summary:This paper investigates the use of evolutionary multi-objective optimization methods (EMOs) for solving single-objective optimization problems in dynamic environments. A number of authors proposed the use of EMOs for maintaining diversity in a single objective optimization task, where they transform the single objective optimization problem into a multi-objective optimization problem by adding an artificial objective function. We extend this work by looking at the dynamic single objective task and examine a number of different possibilities for the artificial objective function. We adopt the non-dominated sorting genetic algorithm version 2 (NSGA2). The results show that the resultant formulations are promising and competitive to other methods for handling dynamic environments.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2005.1554987