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Dynamic Multiobjective Evolutionary Algorithm With Two Stages Evolution Operation



Multiobjective optimization problems occur in many situations and aspects of the engineering optimization field. In reality, many of the multiobjective optimization problems are dynamic in nature, i.e. their Pareto fronts change with the time or environment parameter; these optimization problems most often are called dynamic multiobjective optimization problem (DMOP). The major problems in solving DMOP are how to track and predict the Pareto optimization solutions and how to get the uniformly distributed Pareto fronts, which change with the time parameter. In this paper, a new dynamic multi-objective optimization evolutionary algorithm with two stages evolution operation is proposed for solving the kind of dynamic multiobjective optimization problem in which the Pareto optimal solutions change with time parameter continuously and slowly. At the first stage, when the time parameter has been changed, we use a new core distribution estimation algorithm to generate the new evolution population in the next environment; at the second stage, when the environment of the optimization problem keeps unchanged, a new crossover operator and a mutation operator are used to search the Pareto optimal solutions in current environment. Moreover, three performance metric methods for DMOP based on the generation distance, the spacing and the error ratio are also given. The computer simulations are made on three dynamic multi-objective optimization problems, and the results indicate the proposed algorithm is effective for solving DMOP.



Total Pages: 14
Pages: 575-588


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Volume: 21
Issue: 4
Year: 2015

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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