Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

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