Multi-space evolutionary search with dynamic resource allocation strategy for large-scale optimization
Neural Computing and Applications, ISSN: 1433-3058, Vol: 34, Issue: 10, Page: 7673-7689
2022
- 4Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Multi-space evolutionary search (MSES) is a recently proposed paradigm to optimize across multiple solution spaces for solving a large-scale optimization problem. MSES allocates the computational resource equally on each search space. However, different search spaces are likely to make different contributions to the finding of global optimum of the given problem. Dividing the limited resources equally on each search space in MSES is thus not an efficient strategy. This paper aims to investigate how to utilize the imbalanced efforts to allocate computational resources in multiple search spaces to efficiently improve the performance of MSES. In this paper, we propose a novel multi-space evolutionary search with dynamic resource allocation strategy (MSES - DRA) for large-scale optimization. In particular, a detection mechanism is presented to measure the reasonableness of assignment in terms of computation resource of different spaces. Further, according to the interaction between optimal individual and population, the proposed dynamic resource allocation strategy is designed based on the explicit–implicit contributions of spaces. The explicit and implicit contributions are defined by the fitness improvement of best solution and the survival of individuals, respectively. An adaptive technology based on the feedback is conducted to balance the assignment of computational resources for each search space. To evaluate the performance of the proposed method, comprehensive empirical experiments have been conducted on the CEC2013 large-scale benchmark problems.
Bibliographic Details
Springer Science and Business Media LLC
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