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Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm

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Abstract

A novel dynamic multi-objective optimization evolutionary algorithm is proposed in this paper to track the Pareto-optimal set of time-changing multi-objective optimization problems. In the proposed algorithm, to initialize the new population when a change is detected, a modified prediction model utilizng the historical optimal sets obtained in the last two times is adopted. Meantime, to improve both convergence and diversity, a self-adaptive differential evolution crossover operator is used. We conducted two experiments: the first one compares the proposed algorithm with the other three dynamic multiobjective evolutionary algorithms, and the second one investigates the performance of the two proposed operators. The statistical results indicate that the proposed algorithm has better conergence speed and diversity and it is very promising for dealing with dynamic environment.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Ruochen Liu, Jing Fan & Licheng Jiao

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  1. Ruochen Liu
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  2. Jing Fan
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  3. Licheng Jiao
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Correspondence to Ruochen Liu.

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Liu, R., Fan, J. & Jiao, L. Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm. Appl Intell 43, 192–207 (2015). https://doi.org/10.1007/s10489-014-0625-y

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