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Jellyfish Search Optimizer/Evidence
Method evidence record

Jellyfish Search Optimizer

The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement.

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Jellyfish Search Optimizer
Taxonomic method record · ml-model / optimization
  • Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketAquila Optimizermachine-suggested · Relational suggestion, not evidence.Used in the same domainParticle Swarm Optimizationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSlime Mould Algorithmmachine-suggested · Relational suggestion, not evidence.

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Sources

1 recorded citation, copied from the method source record.

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