Abstract
Multi-objective optimization is a significant topic since many real-world problems consider different aspects. The potential conflicts among the aspects make optimization even more difficult. The MOEA/D-CMAES has shown its capability in tackling complex multi-objective optimization problems. However, MOEA/D-CMAES needs to limit the offspring population size of each subproblem to save computational cost, which causes its vulnerability to premature convergence due to the deficiency of sampling points. This study aims to address this issue with two features: fitness inheritance and information sharing. More specifically, fitness inheritance is used to reduce the computational cost at fitness evaluation and therefore enables a larger size of offspring population. In addition, information sharing facilitates communication and utilization of offspring information among different subproblems. A series of experiments are conducted on the complex multi-objective problems. The experimental results show that the proposed MOEA/D-FICMAES is effective and efficient in solving the complex multi-objective optimization problems, in comparison to two decomposition based and one fitness inheritance assisted multiobjective optimization evolutionary algorithms.
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