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Jellyfish search optimizer

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Visualization of JS for searching the global minimum of a mathematical function.

In computer science and operation research, the jellyfish search (JS) algorithm is an optimimization algorithm based on the behavior of jellyfish in ocean. The JS algorithm is developed by Jui-Sheng Chou and Dinh-Nhat Truong in 2021 [1].


Jellyfish have features that enable them to control their movements. Despite this ability, they mostly drift in the water, depending on currents and tides [2]. When conditions are favorable, jellyfish can form a swarm, and a large mass of jellyfish is called a jellyfish bloom . Numerous factors govern the formation of swarm, including ocean currents, available nutrients, oxygen availability, predation, and temperature. Among these factors, ocean currents are the most important as they can collect jellyfish into a swarm [3][4][5].

This phenomenon, along with each jellyfish's own movements inside the swarm and following ocean current to form jellyfish bloom, has given these species the ability to appear almost everywhere in the ocean [6]. The quantity of food at sites that are visited by a jellyfish varies; thus, when food proportions are compared, the best location would be identified. Therefore, a new algorithm that is inspired by search behavior and movement of jellyfish in the ocean is developed herein. It is named jellyfish search optimizer. Figure in [1] presents the steps of the algorithm.


The proposed optimization algorithm is based on three idealized rules [1]:

  1. Jellyfish either follow the ocean current or move inside the swarm, and a “time control mechanism” governs the switching between these types of movement.
  2. Jellyfish move in the ocean in search of food. They are more attracted to locations where the available quantity of food is greater.
  3. The quantity of food found is determined by the location and its corresponding objective function.

The psedo-code of JS is presented below [1].

Figure 2: Pseudo-code of JS algorithm.

Detail of JS is given by Chou and Truong [1], and demo MATLAB program is available in Mathworks and ResearchGate .

See also[edit]

List of metaphor-based metaheuristics


  1. 1.0 1.1 1.2 1.3 1.4 Chou, Jui-Sheng; Truong, Dinh-Nhat (2021-01-15). "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean". Applied Mathematics and Computation. 389: 125535. doi:10.1016/j.amc.2020.125535. ISSN 0096-3003.
  2. Fossette, Sabrina; Putman, Nathan F.; Lohmann, Kenneth J.; Marsh, Robert; Hays, Graeme C. (2012-06-21). "A biologist's guide to assessing ocean currents: a review". Marine Ecology Progress Series. 457: 285–301. Bibcode:2012MEPS..457..285F. doi:10.3354/meps09581. ISSN 0171-8630.
  3. Brotz, Lucas; Cheung, William W. L.; Kleisner, Kristin; Pakhomov, Evgeny; Pauly, Daniel (2012-07-01). "Increasing jellyfish populations: trends in Large Marine Ecosystems". Hydrobiologia. 690 (1): 3–20. doi:10.1007/s10750-012-1039-7. ISSN 1573-5117. Unknown parameter |s2cid= ignored (help)
  4. Dong, Zhijun; Liu, Dongyan; Keesing, John K. (2010-07-01). "Jellyfish blooms in China: Dominant species, causes and consequences". Marine Pollution Bulletin. 60 (7): 954–963. doi:10.1016/j.marpolbul.2010.04.022. ISSN 0025-326X. PMID 20553695.
  5. Fossette, Sabrina; Gleiss, Adrian Christopher; Chalumeau, Julien; Bastian, Thomas; Armstrong, Claire Denise; Vandenabeele, Sylvie; Karpytchev, Mikhail; Hays, Graeme Clive (2015-02-02). "Current-Oriented Swimming by Jellyfish and Its Role in Bloom Maintenance". Current Biology. 25 (3): 342–347. doi:10.1016/j.cub.2014.11.050. ISSN 0960-9822. PMID 25619761. Unknown parameter |s2cid= ignored (help)
  6. Richardson, Anthony J.; Bakun, Andrew; Hays, Graeme C.; Gibbons, Mark J. (1 June 2009). "The jellyfish joyride: causes, consequences and management responses to a more gelatinous future". Trends in Ecology & Evolution. 24 (6): 312–322. doi:10.1016/j.tree.2009.01.010. ISSN 0169-5347. PMID 19324452.

External links[edit]

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