An evolutionary algorithm for dynamic multi-objective optimization

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Original languageEnglish
Pages (from-to)6-18
Journal / PublicationApplied Mathematics and Computation
Issue number1
Publication statusPublished - 1 Nov 2008


In this paper, the dynamic multi-objective optimization problem (DMOP) is first approximated by a series of static multi-objective optimization problems (SMOPs) by dividing the time period into several equal subperiods. In each subperiod, the dynamic multi-objective optimization problem is seen as a static multi-objective optimization problem by taking the time parameter fixed. Then, to decrease the amount of computation and efficiently solve the static problems, each static multi-objective optimization problem is transformed into a two-objective optimization problem based on two re-defined objectives. Finally, a new crossover operator and mutation operator adapting to the environment changing are designed. Based on these techniques, a new evolutionary algorithm is proposed. The simulation results indicate that the proposed algorithm can effectively track the varying Pareto fronts with time. © 2008.

Research Area(s)

  • Dynamic multi-objective programming, Evolutionary algorithm, Uniform design