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Mathematical Problems in Engineering
Volume 2018, Article ID 6906295, 16 pages
https://doi.org/10.1155/2018/6906295
Research Article

Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China

Correspondence should be addressed to Weijia Cui; nc.ude.aaub@aijiewiuc

Received 20 November 2017; Revised 13 February 2018; Accepted 25 February 2018; Published 8 April 2018

Academic Editor: Ricardo Soto

Copyright © 2018 Weijia Cui and Yuzhu He. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.