Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8190
Title: Modified global flower pollination algorithm and its application for optimization problems
Authors: Shambour, Moh’d Khaled Yousef 
Abusnaina, Ahmed A. 
Alsalibi, Ahmed I. 
Keywords: Computer algorithms;Swarm intelligence;Flower Pollination Algorithm;Computational intelligence;Optimization problems;Mathematical optimization;Calculus of variations;Exploration;Artificial neural networks;Neural networks (Computer science);Pattern recognition systems
Issue Date: 2019
Abstract: Flower Pollination Algorithm (FPA) has increasingly attracted researchers’ attention in the computational intelligence field. This is due to its simplicity and efficiency in searching for global optimality of many optimization problems. However, there is a possibility to enhance its search performance further. This paper aspires to develop a new FPA variant that aims to improve the convergence rate and solution quality, which will be called modified global FPA (mgFPA). The mgFPA is designed to better utilize features of existing solutions through extracting its characteristics, and direct the exploration process towards specific search areas. Several continuous optimization problems were used to investigate the positive impact of the proposed algorithm. The eligibility of mgFPA was also validated on real optimization problems, where it trains artificial neural networks to perform pattern classification. Computational results show that the proposed algorithm provides satisfactory performance in terms of finding better solutions compared to six state-of-the-art optimization algorithms that had been used for benchmarking.
URI: http://hdl.handle.net/20.500.11889/8190
Appears in Collections:Fulltext Publications

Show full item record

Page view(s)

15
checked on Jun 27, 2024

Download(s)

11
checked on Jun 27, 2024

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.