Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5854
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: Flower pollination algorithm
Swarm intelligence
Natural computation
Artificial intelligence
Computational intelligence
Evolutionary computation
Mathematical optimization - Data processing
Neural networks (Computer science)
Issue Date: Mar-2018
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.
URI: http://hdl.handle.net/20.500.11889/5854
Appears in Collections:Fulltext Publications (BZU Community)

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