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dc.contributor.authorShambour, Moh’d Khaled Yousef-
dc.contributor.authorAbusnaina, Ahmed A.-
dc.contributor.authorAlsalibi, Ahmed I-
dc.description.abstractFlower 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.en_US
dc.subjectFlower pollination algorithmen_US
dc.subjectSwarm intelligenceen_US
dc.subjectNatural computationen_US
dc.subjectArtificial intelligenceen_US
dc.subjectComputational intelligenceen_US
dc.subjectEvolutionary computationen_US
dc.subjectMathematical optimization - Data processingen_US
dc.subjectNeural networks (Computer science)en_US
dc.titleModified global flower pollination algorithm and its application for optimization problemsen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.general-subjectEngineering and Technology | الهندسة والتكنولوجياen_US
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