Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 3, Pages 1106-1119Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2883914
Keywords
Optimization; Shape; Training; Computer science; Sociology; Statistics; Evolutionary computation; Evolutionary algorithm; fitness function; front modeling; multiobjective and many-objective optimization
Categories
Funding
- National Natural Science Foundation of China [61822301, 61672033, 61502004, 61502001]
- Anhui Provincial Natural Science Foundation for Distinguished Young Scholars [1808085J06]
- Shenzhen Peacock Plan [KQTD2016112514355531]
- U.K. EPSRC [EP/M017869/1]
- EPSRC [EP/M017869/1] Funding Source: UKRI
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In evolutionary multiobjective optimization, the Pareto front (PF) is approximated by using a set of representative candidate solutions with good convergence and diversity. However, most existing multiobjective evolutionary algorithms (MOEAs) have general difficulty in the approximation of PFs with complicated geometries. To address this issue, we propose a generic front modeling method for evolutionary multiobjective optimization, where the shape of the nondominated front is estimated by training a generalized simplex model. On the basis of the estimated front, we further develop an MOEA, where both the mating selection and environmental selection are driven by the approximate nondominated fronts modeled during the optimization process. For performance assessment, the proposed algorithm is compared with several state-of-the-art evolutionary algorithms on a wide range of benchmark problems with various types of PFs and different numbers of objectives. Experimental results demonstrate that the proposed algorithm performs consistently on a variety of multiobjective optimization problems.
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