基于对立学习的多策略花授粉算法

Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018) Pub Date : 2018-10-22 DOI:10.5753/ENIAC.2018.4453

Cácio L. N. A. Bezerra, Fábio G. B. C. Costa, Lucas V. Bazante, P. Carvalho, Fábio A. P. Paiva

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引用次数: 0

摘要

花授粉算法(FPA)已被广泛用于解决优化问题。然而，它面临着局部最优停滞的问题。已经提出了几种方法来处理这个问题。为了提高FPA的性能，本文提出了一种新的变体，该变体将FPA与基于对立的学习(OBL)的两个变体如准OBL (QOBL)和精英OBL (EOBL)相结合。为了评估这个建议，我们使用了10个基准函数。此外，将该算法与原始FPA以及FA-EOBL、SBFPA和DE-FPA三种变体进行了比较。这项建议产生了显著的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Flower Pollination Algorithm Combined with Multiple Strategies of Opposition–Based Learning

Flower Pollination Algorithm (FPA) has been widely used to solve optimization problems. However, it faces the problem of stagnation in local optimum. Several approaches have been proposed to deal with this problem. To improve the performance of the FPA, this paper presents a new variant that combines FPA and two variants of the Opposition Based Learning (OBL), such as Quasi OBL (QOBL) and Elite OBL (EOBL). To evaluate this proposal, 10 benchmark functions were used. In addition, the proposed algorithm was compared with original FPA and three variants such as FA–EOBL, SBFPA and DE–FPA. The proposal presented significant results.

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Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018)

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