Dove Swarm Optimization Algorithm

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

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 languageEnglish
Pages (from-to)46690-46696
Number of pages7
JournalIEEE Access
StatePublished - 2022


  • Swarm intelligence
  • computational intelligence
  • optimization algorithm


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