A novel modified flower pollination algorithm for global optimization

2018 ◽  
Vol 31 (8) ◽  
pp. 3875-3908 ◽  
Author(s):  
Allouani Fouad ◽  
Xiao-Zhi Gao
Keyword(s):  
Global Optimization ◽  
Flower Pollination Algorithm ◽  
Flower Pollination

scholarly journals Modified Flower Pollination Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1661
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Safaa Saber ◽  
S. S. Askar ◽  
Mohamed Abouhawwash
Keyword(s):  
Global Optimization ◽  
Nonlinear Equation ◽  
Differential Evolution ◽  
Flower Pollination Algorithm ◽  
Test Cases ◽  
Test Functions ◽  
Flower Pollination ◽  
New Variant ◽  
Experimental Findings ◽  
Nonlinear Equation Systems

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

Island flower pollination algorithm for global optimization

2019 ◽  
Vol 75 (8) ◽  
pp. 5280-5323 ◽  
Author(s):  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah ◽  
Iyad Abu Doush ◽  
Abdelaziz I. Hammouri ◽  
Majdi Mafarja ◽  
...  
Keyword(s):  
Global Optimization ◽  
Flower Pollination Algorithm ◽  
Flower Pollination

A Hybrid Algorithm Based on Flower Pollination Algorithm and Electro Search for Global Optimization

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Md Fadil Md Esa ◽  
Noorfa Haszlinna Mustaffa ◽  
Nor Haizan Mohamed Radzi
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
High Performance ◽  
Search Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Method ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Local Search Strategy

In this paper, we have presented a new hybrid optimization method called hybrid Electro-Search algorithm (Eo) and Flower Pollination Optimization Algorithm (FPA) which introduces Eo to FPA. EO-FPA combines the merits of both Eo and FPA by designing on the local-search strategy from Eo and global-search strategy from FPA. The results of the experiments performed with twenty-two well-known benchmark functions show that the proposed algorithm possesses outstanding performance in statistical merit as compared to the original and variant FPA. It is proven that the EO-FPA algorithm requires better formulation to achieve efficiency and high performance to work out with global optimization problems.

A comparative study of cuckoo search and flower pollination algorithm on solving global optimization problems

2017 ◽  
Vol 35 (4) ◽  
pp. 588-601 ◽  
Author(s):  
Mohamed Abdel-Basset ◽  
Laila A. Shawky ◽  
Arun Kumar Sangaiah
Keyword(s):  
Global Optimization ◽  
Design Methodology ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Experimental Results ◽  
Flower Pollination Algorithm ◽  
Content Type ◽  
Flower Pollination

Purpose The purpose of this paper is to present a comparison between two well-known Lévy-based meta-heuristics called cuckoo search (CS) and flower pollination algorithm (FPA). Design/methodology/approach Both the algorithms (Lévy-based meta-heuristics called CS and Flower Pollination) are tested on selected benchmarks from CEC 2017. In addition, this study discussed all CS and FPA comparisons that were included implicitly in other works. Findings The experimental results show that CS is superior in global convergence to the optimal solution, while FPA outperforms CS in terms of time complexity. Originality/value This paper compares the working flow and significance of FPA and CS which seems to have many similarities in order to help the researchers deeply understand the differences between both algorithms. The experimental results are clearly shown to solve the global optimization problem.

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