title: A novel multiobjective evolutionary algorithm based on regression analysis.
abstract: As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m - 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m - 1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.
authors: School of Computer, China University of Geosciences, Wuhan 430074, China.
authors: Zhiming
authors: Z
authors: Song
authors: School of Computer, China University of Geosciences, Wuhan 430074, China ; Department of Mechanical & Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK.
authors: Maocai
authors: M
authors: Wang
authors: School of Computer, China University of Geosciences, Wuhan 430074, China.
authors: Guangming
authors: G
authors: Dai
authors: Department of Mechanical & Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK.
authors: Massimiliano
authors: M
authors: Vasile