DocumentCode :
18451
Title :
A Hybrid Framework for Evolutionary Multi-Objective Optimization
Author :
Sindhya, Karthik ; Miettinen, Kaisa ; Deb, Kaushik
Author_Institution :
Dept. of Math. Inf. Technol., Univ. of Jyvaskyla, Agora, Finland
Volume :
17
Issue :
4
fYear :
2013
fDate :
Aug. 2013
Firstpage :
495
Lastpage :
511
Abstract :
Evolutionary multi-objective optimization algorithms are widely used for solving optimization problems with multiple conflicting objectives. However, basic evolutionary multi-objective optimization algorithms have shortcomings, such as slow convergence to the Pareto optimal front, no efficient termination criterion, and a lack of a theoretical convergence proof. A hybrid evolutionary multi-objective optimization algorithm involving a local search module is often used to overcome these shortcomings. But there are many issues that affect the performance of hybrid evolutionary multi-objective optimization algorithms, such as the type of scalarization function used in a local search and frequency of a local search. In this paper, we address some of these issues and propose a hybrid evolutionary multi-objective optimization framework. The proposed hybrid evolutionary multi-objective optimization framework has a modular structure, which can be used for implementing a hybrid evolutionary multi-objective optimization algorithm. A sample implementation of this framework considering NSGA-II, MOEA/D, and MOEA/D-DRA as evolutionary multi-objective optimization algorithms is presented. A gradient-based sequential quadratic programming method as a single objective optimization method for solving a scalarizing function used in a local search is implemented. Hence, only continuously differentiable functions were considered for numerical experiments. The numerical experiments demonstrate the usefulness of our proposed framework.
Keywords :
genetic algorithms; quadratic programming; search problems; MOEA/D-DRA; NSGA-II; gradient-based sequential quadratic programming method; hybrid evolutionary multiobjective optimization algorithm; local search module; modular structure; scalarization function; Clustering algorithms; Convergence; Indexes; Pareto optimization; Search problems; Vectors; MOEA/D; MOEA/D-DRA; Memetic optimization; NSGA-II; Pareto optimality; multicriteria optimization; multiple criteria decision making (MCDM);
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/TEVC.2012.2204403
Filename :
6216406
Link To Document :
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