herd intelligence (SI) is a subfield of AI that studies how self-organizing communities of agents exhibit emergent collective behavior. Observations of social behavior in nature, such as migrating birds and fish, had a significant impact on the development of SI.
“Some people call this artificial intelligence, but in reality, this technology enhances us. So instead of artificial intelligence, it enhances our intelligence.” – Ginny Rometty.
swarm intelligence system
A swarm intelligence system often consists of a collection of significant agents that interact locally with each other and with their surroundings. Again, nature, especially biological systems, is often a source of inspiration. Although there is no centralized control structure that determines how individual agents behave, the local, somewhat random interaction between such agents creates an “intelligent” global behavior unknown to each agent. will be created.
Flocks of birds, foraging ants, and schools of fish are all examples of swarm intelligence in nature. Swarm Intelligence Algorithms (SIA) are a class of algorithms that take cues from swarm behavior to solve optimization problems.
swarm robotics
Swarm robotics refers to the application of swarm principles to robots, while swarm intelligence refers to a broader set of algorithms. For example, herd predictions have been used to predict difficulties. In synthetic collective intelligence, methodologies similar to those described for swarm robots are being studied for genetically engineered organisms.
Particle swarm optimization algorithm
The first swarm-based algorithm is particle swarm optimization. This algorithm allows people belonging to basic life structures to unite to form more intelligent systems.
Step1: Randomly initialize Swarm population of N particles Xi ( i=1, 2, …, n) Step2: Select hyperparameter values w, c1 and c2 Step 3: For Iter in range(max_iter): # loop max_iter times For i in range(N): # for each particle: a. Compute new velocity of ith particle swarm[i].velocity = w*swarm[i].velocity + r1*c1*(swarm[i].bestPos - swarm[i].position) + r2*c2*( best_pos_swarm - swarm[i].position) b. Compute new position of ith particle using its new velocity swarm[i].position += swarm[i].velocity c. If position is not in range [minx, maxx], then clip it if swarm[i].position < minx: swarm[i].position = minx elif swarm[i].position > maxx: swarm[i].position = maxx d. Update new best of this particle and new best of Swarm if swaInsensitive to scaling of design variables.rm[i].fitness < swarm[i].bestFitness: swarm[i].bestFitness = swarm[i].fitness swarm[i].bestPos = swarm[i].position if swarm[i].fitness < best_fitness_swarm best_fitness_swarm = swarm[i].fitness best_pos_swarm = swarm[i].position End-for End -for Step 4: Return best particle of Swarm
Algorithm source: otaku mania
artificial swarm intelligence
The term “artificial swarm intelligence” (ASI) refers to techniques that enhance the collective intelligence of networked human organizations using control algorithms inspired by natural swarms. Human swarming, also known as Swarm AI, is a form of crowdsourcing where many people discuss a problem and eventually he agrees on one answer in order to work together in real time to solve the problem. ASI is being used for everything from helping business teams make more accurate financial forecasts to helping sports fans win in Las Vegas. ASI is also being used to enable teams of clinicians to make diagnoses with much greater accuracy than before.
Swarm Intelligence application
In addition to classical optimization problems, swarm intelligence has applications in library acquisition, communications, classification of medical data sets, dynamic control, heating system planning, and locomotive monitoring and prediction. In addition, swarm intelligence has applications in basic research, engineering, business, and social sciences.
- Data mining uses this term.
- NASA is developing a planetary mapping concept using swarm intelligence.
- It is used to control nanobots in the body to remove cancerous tumors.
- It is used in business, especially to make better financial decisions.
