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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Adaptive multi-context cooperatively coevolving in differential evolution
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Adaptive multi-context cooperatively coevolving in differential evolution

机译：自适应多语境协同互动地共同

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摘要

This paper presents an adaptive multi-context cooperatively coevolving differential evolution (AMCC-DE) algorithm, in order to address the issue of scaling up differential evolution algorithms on large-scale global optimization (LSGO) problems. The proposed AMCC-DE builds on the success of an early AMCCPSO in which the adaptive multi-context cooperatively coevolving (AMCC) framework is employed. In the proposed AMCC-DE, several superior individuals are employed as the multiple context vectors (CV) to provide robust and effective coevolution, and these CVs are selected by each individual based on their adaptive probabilities. To keep the diversity of these CVs, the mutation operation of CV is defined and conducted in each generation. Moreover, a new mutation operator is also proposed and employed in the AMCC-DE to generate promising individuals. On a comprehensive set of 1000-dimensional LSGO benchmarks, the performance of AMCC-DE compared favorably against some state-of-the-art evolutionary algorithms. Experimental results indicate that the proposed AMCC-DE is effective on LSGO problems, and the proposed mechanisms in AMCC-DE can also be generally extended to other EAs.
机译：本文介绍了一个自适应多上下文协同共同差分演进（AMCC-DE）算法，以解决大规模全局优化（LSGO）问题上缩放差分演进算法的问题。所提出的AMCC-DE构建了早期AMCCPSO的成功，其中采用自适应多上下文协同共同（AMCC）框架。在所提出的AMCC-DE中，使用几种优异的个体作为多种​​上下文向量（CV）以提供稳健且有效的参数，并且这些CVS根据其自适应概率选择每个单独的。为了保持这些CV的多样性，在每一代中定义和进行CV的突变操作。此外，还提出了一种新的突变算子并在AMCC-de中使用，以产生有前途的个体。在一整套1000维LSGO基准测试中，对某些最先进的进化算法相比，AMCC-DE的性能。实验结果表明，所提出的AMCC-DE对LSGO问题有效，并且AMCC-de中的提议机制也可以延伸到其他EA。

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