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: Contribution to journal › Article › peer-review

18 Scopus citations

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
State	Published - 2022

Keywords

Swarm intelligence
computational intelligence
optimization algorithm

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10.1109/ACCESS.2022.3170112

Establishing Smart Cloud-Iot Interaction Platform for Construction Value Chain Using Sip, Association Rules, and Deep-Learning Computational Approach(3/3)
Chen, J.-H. (PI)
1/08/21 → 31/07/22
Project: Research
運用巨量交易資料及人機互動計算建構開發工程專案之先進智慧報價行為系統(1/3)
Chen, J.-H. (PI)
1/08/21 → 31/07/22
Project: Research
提升營建鋼構場生產智慧化系統之先進開發研究(2/3)
Chen, J.-H. (PI)
1/06/21 → 31/05/22
Project: Research

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

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

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doi = "10.1109/ACCESS.2022.3170112",

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volume = "10",

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TY - JOUR

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

PY - 2022

Y1 - 2022

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.

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KW - optimization algorithm

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U2 - 10.1109/ACCESS.2022.3170112

DO - 10.1109/ACCESS.2022.3170112

M3 - 期刊論文

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SN - 2169-3536

VL - 10

SP - 46690

EP - 46696

JO - IEEE Access

JF - IEEE Access

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Dove Swarm Optimization Algorithm

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Keywords

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Projects

Establishing Smart Cloud-Iot Interaction Platform for Construction Value Chain Using Sip, Association Rules, and Deep-Learning Computational Approach(3/3)

運用巨量交易資料及人機互動計算建構開發工程專案之先進智慧報價行為系統(1/3)

提升營建鋼構場生產智慧化系統之先進開發研究(2/3)

Cite this