Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

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Original languageEnglish
Pages (from-to)574-587
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number1
Online published15 Nov 2018
Publication statusPublished - Jan 2021


Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.

Research Area(s)

  • Constrained optimization problems (COPs), decomposition, evolutionary algorithms (EAs), Linear programming, multiobjective optimization, Optimization methods, Pareto dominance, Sociology, Standards, Statistics, Urban areas