A Shift Vector Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Dynamic Optimization

This paper presents a novel algorithm to deal with dynamic multiobjective optimization problems, in which the objective functions change over time. The algorithm adopts the decomposition framework to decompose the multiobjective optimization problems into a number of scalar optimization subproblems. For each subproblem, its respective solutions obtained in several former consecutive environments can form a moving trajectory over time. A shift vector guided prediction model is proposed, which samples three intermediately previous solutions of each subproblem to construct two shift vectors. The shift vectors use the weighted summation to generate a new shift vector as the forthcoming motion of the target solution. Then a new location in the later environment is estimated based on the current location and the newly generated shift vector. When detecting an environmental change, the multiobjective evolutionary algorithm based on decomposition will update the population using the predicted solutions by the proposed model. Empirical results demonstrate that our proposed algorithm is effective in tracking dynamic optimal solutions and shows great superiority comparing with state-of-the-art methods.

Abstract licence: CC BY-NC-ND

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