Multi-Objective Differential Evolution with Self-Navigation


Traditional differential evolution (DE) mutation operators explore the search space with no considering the information about the search directions, which results in a purely stochastic behavior. This paper presents a DE variant with self-navigation ability for multi-objective optimization (MODElSN). It maintains a pool of well designed DE mutation operators with distinct search behaviors and applies them in an adaptive way according to the feedback information from the optimization process. Moreover, we deploy the neural network, which is trained by the extreme learning machine, for mapping an artificially generated solution in the objective space back into the decision space. Empirical results demonstrate that MODEISN outperforms several state-of-the-art algorithms on a set of benchmark problems with variable linkages.