Dove Swarm Optimization Algorithm

Mu Chun Su; Jieh Haur Chen; Andina Mugi Utami; Shih Chieh Lin; Hsi Hsien Wei

doi:10.1109/ACCESS.2022.3170112

Dove Swarm Optimization Algorithm

Mu Chun Su, Jieh Haur Chen, Andina Mugi Utami, Shih Chieh Lin, Hsi Hsien Wei

Research output: Journal article publication › Journal article › Academic research › peer-review

18 Citations (Scopus)

Abstract

Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm Optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.

Original language	English
Pages (from-to)	46690-46696
Number of pages	7
Journal	IEEE Access
Volume	10
DOIs	https://doi.org/10.1109/ACCESS.2022.3170112
Publication status	Published - Apr 2022

Keywords

computational intelligence
optimization algorithm
Swarm intelligence

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering

More information

10.1109/ACCESS.2022.3170112

Cite this

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title = "Dove Swarm Optimization Algorithm",

abstract = "Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm Optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems. ",

keywords = "computational intelligence, optimization algorithm, Swarm intelligence",

author = "Su, {Mu Chun} and Chen, {Jieh Haur} and Utami, {Andina Mugi} and Lin, {Shih Chieh} and Wei, {Hsi Hsien}",

note = "Funding Information: This work was supported in part by the Ministry of Science and Technology (MOST), Taiwan, under their Promoting Academic Excellent of Universities Program under Grant MOST 110-2622-E-008-018-CC2, Grant 110-2221-E-008-052-MY3, Grant 109-2634-F-008-007, Grant 108-2221-E-008-002-MY3, and Grant 107-2221-E-008-084-MY2. Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2022",

month = apr,

doi = "10.1109/ACCESS.2022.3170112",

language = "English",

volume = "10",

pages = "46690--46696",

journal = "IEEE Access",

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T1 - Dove Swarm Optimization Algorithm

AU - Su, Mu Chun

AU - Chen, Jieh Haur

AU - Utami, Andina Mugi

AU - Lin, Shih Chieh

AU - Wei, Hsi Hsien

N1 - Funding Information: This work was supported in part by the Ministry of Science and Technology (MOST), Taiwan, under their Promoting Academic Excellent of Universities Program under Grant MOST 110-2622-E-008-018-CC2, Grant 110-2221-E-008-052-MY3, Grant 109-2634-F-008-007, Grant 108-2221-E-008-002-MY3, and Grant 107-2221-E-008-084-MY2. Publisher Copyright: © 2013 IEEE.

PY - 2022/4

Y1 - 2022/4

N2 - Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm Optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.

AB - Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm Optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.

KW - computational intelligence

KW - optimization algorithm

KW - Swarm intelligence

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DO - 10.1109/ACCESS.2022.3170112

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SN - 2169-3536

VL - 10

SP - 46690

EP - 46696

JO - IEEE Access

JF - IEEE Access

ER -

Dove Swarm Optimization Algorithm

Abstract

Keywords

ASJC Scopus subject areas

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