Jellyfish Hunting Optimiser
Following the topic of swarm intelligence algorithms that are specifically based on animal behaviours another new idea is the jellyfish hunting optimiser, it runs, as you would imagine, very similarly to other algorithms i.e. the Cheetah Optimiser. This follows the way in which jellyfish survive, as they are mostly passive in their existence following currents and in general not having much in the way of movement, this allows them to not expend much energy but leaves them to nature to float them towards nutrients and food. Jellyfish do however survive and are in vast quantity, so we can take from this that their ability to find prey and swarm and use it to influence a new algorithm.
The way in which this algorithm looks at jellyfish and their ability to survive is the main influence. They behave very differently to most animals in the sea as they are weak swimmers. they can control their movements but for mass travel rely entirely on currents to pull them along. jellyfish can form large jellyfish blooms when currently lead the organisms together, it is also due to these currents that the swarms do not become stranded. The availability of nutrients, oxygen, predators and temperature all govern the size of the swarm. Here is an image depicting the way in which jellyfish search.
This is the flowchart for the code being run by this paper Flowchart
