The obvious one is neural networks :) This is a very interesting question, and even though I can't answer from personal experience, here is a paper[0] that lis

The obvious one is neural networks :) This is a very interesting question, and even though I can't answer from personal experience, here is a paper[0] that listed the following: >Genetic Bee Colony (GBC) Algorithm This appears to combine genetic algorithms with "swarm intelligence" - several agents, which model the behavior of bees - some scout and share information, others only go for the nearest food source. It's used to predict gene expression for cancer classification. 3 minute video visualization - https://www.youtube.com/watch?v=3qQr1eZwz5E https://www.youtube.com/watch?v=3qQr1eZwz5E >Fish Swarm Algorithm (FSA) Also known as Particle Swarm Optimization. Here the agents behave like individual fish in a school of fish. I.e. every agent tries to be in the center of the school, to avoid a hypothetical predator, and balances that behavior with the desire to eat food. Vis - https://www.youtube.com/watch?v=OUHAypWn1Ro https://www.youtube.com/watch?v=OUHAypWn1Ro >Cat Swarm Optimization (CSO) From another paper: "The original cat swarm optimization is a continuous and single-objective algorithm. It is inspired by resting and tracing behaviours of cats. Cats seem to be lazy and spend most of their time resting. However, during their rests, their consciousness is very high and they are very aware of what is happening around them. So, they are constantly observing the surroundings intelligently and deliberately and when they see a target, they start moving towards it quickly. Therefore, CSO algorithm is modeled based on combining these two main deportments of cats. CSO algorithm is composed of two modes, namely, tracing and seeking modes. Each cat represents a solution set, which has its own position, a fitness value, and a flag. The position is made up of M dimensions in the search space, and each dimension has its own velocity; the fitness value depicts how well the solution set (cat) is; finally, the flag is to classify the cats into either seeking or tracing mode. Thus, we should first specify how many cats should be engaged in the iteration and run them through the algorithm. The best cat in each iteration is saved into memory, and the one at the final iteration will represent the final solution." >Whale Optimization Algorithm (WOA) WOA mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. 10 minute explanation - https://www.youtube.com/watch?v=f7hvvDkLoHs https://www.youtube.com/watch?v=f7hvvDkLoHs >Artificial Algae Algorithm (AAA) >Elephant Search Algorithm (ESA) >Chicken Swarm Optimization Algorithm (CSOA) >Moth flame optimization (MFO) >Grey Wolf Optimization (GWO) algorithm, which mimic the hunting technique and social leadership of grey wolves >Social Spider Optimization (SSO) Algorithm >Lion Optimization Algorithm (LOA) that mimics the behavior of lions and their characteristics of cooperation. Other algorithms that I found: > Ant swarm algorithm An approximate solution to the travelling salesman problem. Say you have many agents that have to visit hundreds of thousands of locations, and each day some are added and removed. It is prohibitively expensive to calculate anew each day, so instead you do like ants. Each agent chooses a path somewhat at random, but also leaves a "pheromone trail" that decays with time. More efficient paths take less time, are traversed more times per day, and thus have heavier scent. Next time an agent considers two paths, it will be biased towards the path with a heavier scent. So you arrive at an approximately efficient solution. >firefly algorithm https://www.youtube.com/watch?v=LPKg_luHxpc https://www.youtube.com/watch?v=LPKg_luHxpc >eagle strategy https://www.youtube.com/watch?v=cNEi_P6mUnY https://www.youtube.com/watch?v=cNEi_P6mUnY [0] https://www.sciencedirect.com/science/article/pii/S2314728818300631 https://www.sciencedirect.com/science/article/pii/S231472881...