首页|Ensemble of resource allocation strategies in decision and objective spaces for multiobjective optimization
Ensemble of resource allocation strategies in decision and objective spaces for multiobjective optimization
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NSTL
Elsevier
A well exploitation of computational resource is essential when solving high-dimensional multiobjective problems (MOPs). Since many recent strategies presented for decision space and objective space are in consideration of searching efficiency, the collaboration between the two spaces is a promising approach for high-dimensional optimization. In this article, resource allocation strategies for both decision and objective spaces are readjusted and cooperated for complex MOPs. A metric-based variable partition strategy is introduced and a simple reference adaptation strategy is adopted to specify the searching orientations in the decision space and the objective space respectively. Subsequently, based on the above-mentioned basic techniques, three different evolutionary strategies are further designed to strengthen the directional convergence and preserve the diversity of target regions in a collaborative fashion. Several benchmark instances and a practical optimization problem are adopted in the experimental study. The effectiveness and rationality of the proposed resource allocation approach have been demonstrated by the experimental results.(c) 2022 Elsevier Inc. All rights reserved.
NSTL
Elsevier
