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Flower Pollination Algorithm with Linearly Varying Exploration: A Novel Approach for Continuous Function Optimization Problem

Mohammad Shafiul Alam, Sanonda Datta Gupta, Samiha Sara Prima, Nur-E-Asma Tabassum. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Mohammad Shafiul Alam, Sanonda Datta Gupta, Samiha Sara Prima, Nur-E-Asma Tabassum
10.5120/cae2018652768

Mohammad Shafiul Alam, Sanonda Datta Gupta, Samiha Sara Prima and Nur-E-Asma Tabassum. Flower Pollination Algorithm with Linearly Varying Exploration: A Novel Approach for Continuous Function Optimization Problem. Communications on Applied Electronics 7(16):51-56, May 2018. BibTeX

@article{10.5120/cae2018652768,
	author = {Mohammad Shafiul Alam and Sanonda Datta Gupta and Samiha Sara Prima and Nur-E-Asma Tabassum},
	title = {Flower Pollination Algorithm with Linearly Varying Exploration: A Novel Approach for Continuous Function Optimization Problem},
	journal = {Communications on Applied Electronics},
	issue_date = {May 2018},
	volume = {7},
	number = {16},
	month = {May},
	year = {2018},
	issn = {2394-4714},
	pages = {51-56},
	numpages = {6},
	url = {http://www.caeaccess.org/archives/volume7/number16/815-2018652768},
	doi = {10.5120/cae2018652768},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The Flower Pollination Algorithm (FPA) a recently introduced bio–inspired algorithm based on the natural process of pollination of flowers. FPA has quickly drawn the attention of the research community due to its nature of both exploitation and exploration. In this paper we have introduced a novel variation in its mutation operation by linearly varying the value of its proximate probability parameter. We have evaluated the proposed algorithm on a number of benchmark problems and the experimental results are compared with the basic FPA. The empirical study indicates that the proposed FPA with variable exploration rate has better optimization performance than the basic FPA algorithm on most of the benchmark functions.

References

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Keywords

Flower pollination algorithm; exploration; exploitation; proximate probability.