| Title |
Modified Global Flower Pollination Algorithm and its Application for Optimization Problems
|
|---|---|
| Published in |
Interdisciplinary Sciences: Computational Life Sciences, March 2018
|
| DOI | 10.1007/s12539-018-0295-2 |
| Pubmed ID | |
| Authors |
Moh’d Khaled Yousef Shambour, Ahmed A. Abusnaina, Ahmed I. Alsalibi |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 19% |
| Student > Bachelor | 5 | 14% |
| Student > Doctoral Student | 3 | 8% |
| Professor > Associate Professor | 3 | 8% |
| Student > Master | 3 | 8% |
| Other | 1 | 3% |
| Unknown | 14 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 33% |
| Engineering | 5 | 14% |
| Business, Management and Accounting | 1 | 3% |
| Medicine and Dentistry | 1 | 3% |
| Mathematics | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 16 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 March 2018.
All research outputs
#18,594,219
of 23,031,582 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#166
of 297 outputs
Outputs of similar age
#256,296
of 329,889 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#4
of 6 outputs
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