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首页> 外文期刊>IEEE transactions on industrial informatics >Differential Evolution Algorithm With Two-Step Subpopulation Strategy and Its Application in Microwave Circuit Designs
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Differential Evolution Algorithm With Two-Step Subpopulation Strategy and Its Application in Microwave Circuit Designs

机译：具有两步子种群策略的差分进化算法及其在微波电路设计中的应用

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摘要

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for both single objective and multiobjective optimizations (MOPs). In nature, good parents are more likely to produce good offspring, because genes from good individuals propagate throughout the population. Inspired by this phenomenon, a two-step subpopulation strategy is proposed, in which individuals in the current population are sorted based on evaluation metrics, and are divided into superior and inferior subpopulations. The inferior subpopulation evolves to generate offspring. If the generated offspring has better evaluation metric values than individuals in the superior subpopulation, they will replace the latter and be used as vectors for mutation strategies. The proposed strategy is incorporated into several advanced DE variants for both single-objective optimization (SOP) and MOPs to verify its effectiveness. Experiments are conducted on 25 single objective, 5 bi-objective, and 4 tri-objective Deb, Thiele, Laumanns and Zitzler (DTLZ) benchmark problems. Results indicate that the proposed subpopulation strategy is capable of improving the performance of both single objective and multiobjective algorithms. The application of the proposed approach is demonstrated by solving a microwave circuit design problem with stringent requirements. The better performance achieved by the proposed approach in quality of solutions, convergence rate, and diversity is verified by the performance comparison with competitive optimization algorithms in the literature.
机译：差分进化（DE）是一种简单但功能强大的进化算法，可用于单目标优化和多目标优化（MOP）。在自然界中，好父母更容易产生好后代，因为好人的基因在整个种群中传播。受此现象的启发，提出了一种两步亚种群策略，其中，根据评估指标对当前人群中的个体进行分类，并将其分为上，下亚人群。下亚群进化产生后代。如果生成的后代具有比上等亚群中的个体更好的评估度量值，则它们将替代后者，并用作突变策略的载体。所提出的策略已合并到多个高级DE变量中，以用于单目标优化（SOP）和MOP，以验证其有效性。实验针对25个单目标，5个双目标和4个三目标Deb，Thiele，Laumanns和Zitzler（DTLZ）基准问题进行。结果表明，所提出的子种群策略能够提高单目标算法和多目标算法的性能。通过解决具有严格要求的微波电路设计问题，证明了该方法的应用。通过与文献中的竞争性优化算法进行性能比较，验证了所提方法在解决方案质量，收敛速度和多样性方面获得的更好性能。

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