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An evolutionary algorithm for dynamic multi-objective optimization
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DOI：10.1016/j.amc.2008.05.151.png)
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Abstract
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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. (C) 2008 Published by Elsevier Inc.
Keywords:
Evolutionary algorithm
Dynamic multi-objective programming
Uniform design
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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach
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