期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 19, 期 6, 页码 838-856出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2015.2395073
关键词
Estimation of distribution algorithms (EDAs); Gaussian processes (GPs); inverse modeling; multiobjective optimization (MOO); random grouping
资金
- Honda Research Institute Europe
- Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China [61428302]
- Engineering and Physical Sciences Research Council [EP/M017869/1]
- EPSRC [EP/M017869/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M017869/1] Funding Source: researchfish
To approximate the Pareto front, most existing multiobjective evolutionary algorithms store the nondominated solutions found so far in the population or in an external archive during the search. Such algorithms often require a high degree of diversity of the stored solutions and only a limited number of solutions can be achieved. By contrast, model-based algorithms can alleviate the requirement on solution diversity and in principle, as many solutions as needed can be generated. This paper proposes a new model-based method for representing and searching nondominated solutions. The main idea is to construct Gaussian process-based inverse models that map all found nondominated solutions from the objective space to the decision space. These inverse models are then used to create offspring by sampling the objective space. To facilitate inverse modeling, the multivariate inverse function is decomposed into a group of uni-variate functions, where the number of inverse models is reduced using a random grouping technique. Extensive empirical simulations demonstrate that the proposed algorithm exhibits robust search performance on a variety of medium to high dimensional multiobjective optimization test problems. Additional nondominated solutions are generated a posteriori using the constructed models to increase the density of solutions in the preferred regions at a low computational cost.
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