Weight vector grid with new archive update mechanism for multi-objective optimization
Pages 1906 - 1909
Abstract
Currently, most of the decomposition-based multi-objective evolutionary algorithms (MOEA) are based on a number of prespecified weight vectors. However, when the shape of the Pareto front is inconsistent with the distribution of weight vectors, only a small number of non-dominated solutions can be obtained inside the Pareto front. Moreover, if an external archive with a dominance-based update mechanism is used to overcome this difficulty, a large computational time is needed which is often unpractical. In this paper, we propose a new archive update mechanism with a new archive structure. A large weight vector grid is used to update the archive by using a scalarizing function. The proposed archive update mechanism can be applied to any MOEA with an external archive. We examine the effectiveness of the proposed mechanism on MOEA/D. Our experimental results show that MOEA/D with the proposed new archive update mechanism is able to find more solutions inside the Pareto front compared to MOEA/D without the archive. In addition, it needs less computational time compared to MOEA/D with the dominance-based archive update mechanism.
References
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Index Terms
- Weight vector grid with new archive update mechanism for multi-objective optimization
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July 2018
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Published: 06 July 2018
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