A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems

المؤلفون المشاركون

Qi, Xiangbo
Yuan, Zhonghu
Song, Yan

المصدر

Computational Intelligence and Neuroscience

العدد

المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-25، 25ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2020-05-29

دولة النشر

مصر

عدد الصفحات

25

التخصصات الرئيسية

الأحياء

الملخص EN

Hybridization of metaheuristic algorithms with local search has been investigated in many studies.

This paper proposes a hybrid pathfinder algorithm (HPFA), which incorporates the mutation operator in differential evolution (DE) into the pathfinder algorithm (PFA).

The proposed algorithm combines the searching ability of both PFA and DE.

With a test on a set of twenty-four unconstrained benchmark functions including both unimodal continuous functions, multimodal continuous functions, and composition functions, HPFA is proved to have significant improvement over the pathfinder algorithm and the other comparison algorithms.

Then HPFA is used for data clustering, constrained problems, and engineering design problems.

The experimental results show that the proposed HPFA got better results than the other comparison algorithms and is a competitive approach for solving partitioning clustering, constrained problems, and engineering design problems.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Qi, Xiangbo& Yuan, Zhonghu& Song, Yan. 2020. A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-25.
https://search.emarefa.net/detail/BIM-1138775

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Qi, Xiangbo…[et al.]. A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-25.
https://search.emarefa.net/detail/BIM-1138775

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Qi, Xiangbo& Yuan, Zhonghu& Song, Yan. A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-25.
https://search.emarefa.net/detail/BIM-1138775

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1138775