Enhancing the performance of biogeography-based optimization using multitopology and quantitative orthogonal learning
type: Item
instance of: scholarly article
title: Enhancing the performance of biogeography-based optimization using multitopology and quantitative orthogonal learning
zbMATH Open document ID: 1426.90263
DOI: 10.1155/2017/2314927
author: Siao Wen
author: Yinhong Li
author: Dongyuan Shi
author: Xianzhong Duan
author: Jin-Fu Chen
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 6971855
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/2314927
OpenAlex ID: W2754014346
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description: scientific article; zbMATH DE number 6971855