A Hybrid Framework for Evolutionary Multi-objective Optimization

作者：Sindhya Karthik^*; Miettinen Kaisa; Deb Kalyanmoy

来源：IEEE Transactions on Evolutionary Computation, 2013, 17(4): 495-511.

DOI：10.1109/TEVC.2012.2204403

• Memetic optimization
• MOEA/D
• MOEA/D-DRA
• multicriteria optimization
• multiple criteria decision making (MCDM)
• NSGA-II
• Pareto optimality

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.

• 出版日期2013-8